Teleconferência de Resultados da IBM no 3T26: Crescimento de Software Acelera, Projeção de Fluxo de Caixa Livre É Elevada

Principais Destaques

• A receita do terceiro trimestre cresceu 6% em moeda constante, com o crescimento acelerando nos segmentos de Software, Infraestrutura e Consultoria.
• A receita de Software subiu 9%, incluindo mais de 4 pontos percentuais de crescimento orgânico. A Red Hat cresceu 11%, Data aumentou 20% e Transaction Processing avançou 10%.
• A margem operacional antes dos impostos se expandiu em 70 pontos-base, enquanto o LPA operacional diluído aumentou 11%.
• A IBM gerou aproximadamente US$ 8 bilhões em fluxo de caixa livre nos primeiros nove meses, alta de 8% em relação ao ano anterior, e elevou sua projeção de crescimento do fluxo de caixa livre no ano para mais de US$ 1 bilhão.
• A administração manteve sua projeção de crescimento da receita para 2026 entre 4% e 5% e elevou a previsão de crescimento de Software no ano para aproximadamente 7% a 8%.
• A receita recorrente anual (ARR) do OpenShift atingiu US$ 2,4 bilhões, com alta de mais de 30%, enquanto a nova oferta Lightwell construiu um pipeline próximo de US$ 700 milhões.

Dados Financeiros Principais

Desempenho Operacional e de Negócios

Software

O segmento de Software foi o principal motor de crescimento. A receita aumentou 9%, e três dos quatro subsegmentos de Software da IBM registraram crescimento de dois dígitos.

A Red Hat cresceu 11%, com as reservas (bookings) voltando a crescer na faixa de quase 20%. O ARR do OpenShift atingiu US$ 2,4 bilhões e expandiu mais de 30%. A oferta de virtualização da IBM garantiu quase US$ 800 milhões em contratos desde o início de 2024 e tem um pipeline de cerca de US$ 750 milhões.

A receita de Dados subiu 20%, apoiada pela Confluent, cujas reservas cresceram cerca de 20%. A administração destacou o papel da Confluent no fornecimento de dados em tempo real para aplicações de IA e espera que o Streamhouse crie oportunidades de venda cruzada (cross-selling) e vendas adicionais (upselling).

O segmento de Transaction Processing cresceu 10%. A administração atribuiu a recuperação tanto a transações adiadas do segundo trimestre quanto ao ciclo normal de compras do terceiro trimestre. A IBM fechou cerca de dois terços dos grandes contratos de Software que haviam sido adiados do trimestre anterior, representando mais de US$ 250 milhões. A administração alertou que é improvável que o crescimento de dois dígitos em Transaction Processing continue no mesmo ritmo no quarto trimestre.

O Lightwell alcançou disponibilidade geral e gerou um pipeline próximo a US$ 700 milhões. A IBM afirmou que mais de 40.000 pacotes corrigidos foram disponibilizados para a rede Lightwell.

Infraestrutura

A receita de Infraestrutura subiu 5%, liderada pelo crescimento de 32% em Infraestrutura Distribuída. A área de Armazenamento (Storage) registrou crescimento recorde de receita, com demanda em flash, fusion, fita e armazenamento IBM Z. A IBM também encerrou o trimestre com uma carteira de pedidos pendentes (backlog) expressiva em Infraestrutura Distribuída.

O programa z17 permaneceu mais de US$ 1 bilhão à frente da geração anterior após seis trimestres. A IBM também registrou seu lançamento mais forte de IBM Z de porte intermediário (midrange) em quase duas décadas. A administração informou que os MIPS instalados subiram na faixa de quase 20% após seis trimestres no ciclo, sustentando futuras oportunidades de monetização em Transaction Processing.

Consultoria

A receita de Consultoria aumentou 2%. Estratégia e Tecnologia cresceu 4%, impulsionada pela modernização de aplicações e transformação de dados e IA. Cibersegurança registrou crescimento próximo de dois dígitos no segmento de Operações Inteligentes.

Os trabalhos relacionados à IA representaram aproximadamente 30% da receita trimestral de Consultoria e 35% do backlog. A margem de lucro do segmento de Consultoria caiu ligeiramente, pois a IBM reinvestiu os ganhos de produtividade em ativos digitais e na sua transição para o modelo de Serviços como Software (Services-as-Software).

IA e Computação Quântica

A administração estruturou a demanda corporativa de IA em torno da modernização de aplicações, dados em tempo real, governança, controle de custos e implantação soberana. A IBM afirmou que Red Hat, Confluent, watsonx, HashiCorp e Apptio formam as plataformas centrais que sustentam essa estratégia.

A IBM disse que o LLM-D, um projeto de código aberto desenvolvido com a Red Hat e o Google, pode reduzir o custo de execução de grandes modelos de IA de 5 a 10 vezes por meio do uso mais eficiente da infraestrutura existente de GPUs. A empresa também informou que o Bob agora pode ser executado em ambientes locais (on-premises) para setores regulados e soberanos.

Em Computação Quântica, a IBM reportou 16 engajamentos corporativos durante o ano. Seu processador Nighthawk r2 atingiu 100.000 circuitos por segundo e entregou melhorias de desempenho de até 19 vezes em relação aos sistemas da geração anterior. A IBM também foi selecionada para o Estágio C da Quantum Benchmarking Initiative da DARPA.

Projeções da Administração (Guidance)

A administração afirmou que as premissas de lucro, fluxo de caixa livre e segmentos permanecem ancoradas em um cenário-base de 4% de crescimento da receita no ano cheio. O crescimento esperado do fluxo de caixa livre é impulsionado principalmente pelo EBITDA ajustado, parcialmente compensado por maiores despesas de capital (CapEx), impostos em caixa e despesas financeiras líquidas.

Riscos e Pontos de Atenção

• O segmento de Software Transacional permanece sensível ao cronograma de fechamento de contratos. Embora a IBM tenha fechado cerca de dois terços dos grandes contratos adiados do segundo trimestre, algumas atividades podem se deslocar para o quarto trimestre.
• A administração espera que o crescimento de Transaction Processing se modere em relação à taxa de 10% registrada no terceiro trimestre.
• O segmento de Automação enfrentou uma base de comparação anual mais difícil, enquanto as margens de Consultoria recuaram ligeiramente devido aos reinvestimentos.
• A IBM afirmou que as empresas estão analisando rigorosamente o retorno sobre o investimento (ROI) em IA e podem não obter todos os benefícios sem redesenhar fluxos de trabalho e integrar a IA aos sistemas existentes.
• IA soberana, proteção de dados e controle sobre registros (logs) estão se tornando mais importantes nas discussões com clientes, especialmente para empresas reguladas.
• Despesas de capital mais elevadas, impostos em caixa e despesas financeiras líquidas continuam sendo fatores de compensação ao crescimento do fluxo de caixa livre.

Destaques das Perguntas e Respostas dos Analistas

• Aceleração em Software: A administração afirmou que aproximadamente um quarto da aceleração sequencial de Software veio de contratos anteriormente adiados, enquanto cerca de três quartos refletiram novos negócios.
• Transaction Processing: A IBM caracterizou a fraqueza do trimestre anterior como uma questão de cronograma, em vez de destruição de demanda. Espera-se que MIPS instalados e entregues mais elevados ampliem as oportunidades futuras de monetização, embora a administração não tenha projetado a taxa de crescimento do terceiro trimestre para o quarto trimestre.
• Perspectivas para Software em 2027: A IBM não forneceu projeções oficiais para 2027. No entanto, a administração apontou as reservas da Red Hat, a integração da Confluent, o Lightwell, a virtualização e a capacidade do z17 como potenciais motores de crescimento e disse que se espera que 2027 enfatize as sinergias de crescimento das aquisições.
• Produtividade e margens: A IBM gerou 500 pontos-base de produtividade bruta no trimestre e afirmou que os benefícios acumulados de produtividade agora superam US$ 5,5 bilhões. A empresa continua reinvestindo parte dessa economia em P&D, inovação e capacidades de inserção no mercado (go-to-market).
• Adoção corporativa de IA: A administração disse que os clientes estão se afastando de inúmeros pequenos experimentos e avançando para um menor número de projetos em escala, focados no redesenho de fluxos de trabalho, retornos mensuráveis e controle de custos.
• Aspectos econômicos da IA soberana: A IBM afirmou que os modelos de pesos abertos (open-weight) reduziram a lacuna de desempenho em relação aos modelos de fronteira, oferecendo custos por token significativamente menores, o que apoia a adoção em infraestrutura local (on-premises) e nuvem privada.

Transcrição Completa da Teleconferência de Resultados

Transcrição completa da teleconferência de resultados

Comentários da administração

Operator

Welcome, and thank you for standing by. [Operator Instructions] Today's conference is being recorded. If you have any objections, you may disconnect at this time.

Now I will turn the meeting over to Olympia McNerney, IBM's Global Head of Investor Relations. Olympia, you may begin.

Olympia McNerney

Thank you. I'd like to welcome you to IBM's Third Quarter 2026 Earnings Presentation. I'm Olympia McNerney, and I'm here today with Arvind Krishna, IBM's Chairman, President and Chief Executive Officer; and Jim Kavanaugh, IBM's Senior Vice President and Chief Financial Officer.

We'll post today's prepared remarks and a replay of today's webcast on the IBM investor website within a couple of hours. The earnings presentation is already available. To provide additional information to our investors, our presentation includes certain non-GAAP measures. For example, all of our references to revenue and signings growth are at constant currency. We provided reconciliation charts for these and other non-GAAP financial measures at the end of the presentation, which is posted to our investor website.

Finally, some comments made in this presentation may be considered forward-looking under the Private Securities Litigation Reform Act of 1995. These statements involve factors that could cause our actual results to differ materially. Additional information about these factors is included in the company's SEC filings.

So with that, I'll turn the call over to Arvind.

Arvind Krishna

Thank you for joining us today. Revenue growth in the third quarter accelerated to 6% above the base case we laid out in July. This performance reflects acceleration across our segments, led by software growth of 9%, infrastructure growth of 5% and modest improvement in consulting. .

We entered the quarter focused on disciplined execution, which also drove strong performance in profitability and free cash flow. These results demonstrate the strength of our portfolio the continued value clients place in mission-critical platforms and our confidence in both our strategy and the outlook for the balance of the year.

Technology innovation in both hybrid infrastructure and super intelligence is leading to better business performance. This quarter's results reinforce the strategic choices we have made over the last several years to advance IBM as a software-led hybrid cloud and AI platform company.

In software, Red Hat again grew 11% with bookings growth accelerating back to the high teens. OpenShift ARR growth accelerated to over 30% and as organizations scale super intelligence applications and agents into production, reinforcing our conviction that we remain in the early stages of a long-term application modernization cycle.

Transaction processing grew 10%, underscoring the strategic importance of the mainframe and validating our view that the softness we saw in the second quarter reflected timing rather than underlying demand. Data grew 20%, supported by continued strength in Confluent. Infrastructure was up 5%, driven by continued momentum in distributed infrastructure, growing 32% this quarter.

AI continues to increase demand for storage, and we are gaining share through differentiated offerings across flash, fusion, tape and IBM Z storage, including AI-enabled capabilities that help clients scale and manage data for AI. Consulting revenue grew 2% as clients modernize to deploy AI securely and at scale. The durability of our portfolio remains a defining feature of IBM today.

Z17 continued the strongest innovation cycle in the platform's history, generating more than $1 billion of incremental revenue over 6 quarters compared with prior programs. This reflects the security, reliability, scalability and compelling economics of the platform. As infrastructure costs rise, IBM Z delivers a 2 to 15x total cost of ownership advantage versus off-platform alternatives, positioning us to capture additional value as AI workloads grow.

Building on that advantage, we introduced new single-frame and rack mount IBM Z systems in July that help clients address data center space, cost and deployment constraints without compromising performance or security. Client response has been strong, contributing to the highest midrange revenue launch in IBM Z this quarter in nearly 2 decades.

Last quarter, we told you Lightwell represents a multibillion-dollar opportunity for IBM and Red Hat to help clients secure their open source software. Since then, some of the world's largest enterprises have signed up as clients, and we have built a pipeline approaching $700 million in a short period of time. We have released more than 40,000 remediated packages to the Lightwell network, demonstrating the rapid scale-up of our remediation capabilities.

Now turning to our AI strategy. Enterprise AI is entering its next phase, deploying more models or building more agents will not be enough. The advantage we belong to a company that can operate AI securely efficiently and sustainably across the enterprise. Organizations are recognizing that AI is only as effective as the system around it. The data that provides context, the workflows that orchestrate action the infrastructure that secures and optimizes operations and the hybrid cloud foundation that provides flexibility without locking.

IBM has assembled these capabilities across 4 platforms. Confluent provides continuous business context through a real-time data foundation. Watsonx, including Bob and orchestrate, support AI-driven software development and agent orchestration. HashiCorp and Apptio automate identity, access and policy while optimizing cost and performance. Red Hat provides the open hybrid architecture, spanning public cloud, on-premises sovereign environments and the edge. Together, these platforms help clients move from isolated deployments to AI operating at enterprise scale.

Data sits at the center of the AI operating model, 72% of organizations report insufficient real-time data infrastructure, which holds back AI at scale. Agents need a current state of the business. That is why we are excited about Streamhouse a capability from Confluent that continuously captures, transforms and queries real-time data. It complements warehouses at Lake houses.

A Streamhouse makes real-time governed data continuously available to applications, analytics and AI, unifying operational and analytical data into a single architecture. We believe Confluent is uniquely positioned with a complete open and hybrid approach that helps clients avoid lock-in while deploying wherever the data resides. As AI scales, clients ask 2 questions, what will it cost and who controls it? We are innovating on both.

Apptio now connects AI spend to business results, providing visibility into AI usage, token consumption and the cost of running it across the enterprise. LLM-D an open source project developed with Red Hat and Google, reusers previously processed context, helping clients run large AI models at 5 to 10x lower cost by making more efficient use of existing GPU infrastructure. And Bob now runs on-prem. So clients in regulated and sovereign environments can keep good and models on their own infrastructure.

Governance matters as much as intelligence. As agents multiply, enterprises need to know who an agent is, what authority it has and what it can access HashiCorp Vault allows clients to register, authorize, audit and revoke agents and secure their credentials. IBM Agent Identity and HashiCorp Vault are partnering with NVIDIA open agent safety platform to strengthen agent security. We believe agent identity will become as foundational to enterprise AI and as identity has become to the Internet.

Finally, Quantum remains a strategic area of differentiation for IBM and a significant long-term opportunity. In July, 3 independent demonstrations of Quantum Advantage were achieved on IBM hardware with the University of Chicago, algorithmic and EDA providing further evidence that quantum computing is moving from scientific promise towards practical value.

Our Nighthawk r2 processor has achieved important road map milestones across speed, scale and quality, delivering 7,500 gigs, executing 100,000 circuits per second and enabling performance improvements of up to 19x over prior generation systems. That performance changes both time solution and the economics of quantum competition. Workloads costing approximately $100 to run an IBM could cost up to $10 million on slower competitive modalities.

Recently, IBM was selected for Stage C, the third phase of the Quantum benchmarking initiative led by DARPA the United States Defense Advanced Research Projects Agency, another validation of the strength of IBM Spark to building a fault-tolerant quantum computer. Commercial momentum is also growing. This year, IBM Quantum has signed 16 enterprise engagements across industries, including automotive, aerospace, telecommunications, financial services and manufacturing.

This includes AT&T and General Motors amongst many others who are now all part of the IBM Quantum network, the world's largest network of quantum innovators. We are also advancing the technology and infrastructure required for industrial scale, fault tolerant computing, including the successful connection of our first modular cryogenic systems and continued progress on our 300,000 square foot for Kips manufacturing facility. This, along with the closing of the $1 billion award from the Department of Commerce in and Deron for manufacturing quantum wafers gives us confidence that after the stalling demonstration, we are ready to scale. We look forward to talking more about our progress at our Quantum Day on December 10.

In closing, our third quarter performance reflects the strength of our portfolio and the progress we are making against our strategic priorities of delivering both sustainable revenue growth and higher profitability. Our results reinforce our confidence in delivering revenue growth for 2026 in a range of 4% to 5% and enabled us to raise our year-over-year free cash flow growth expectation to more than $1 billion.

With that, let me hand it over to Jim.

James Kavanaugh

Thanks, Arvind. In the third quarter, we delivered solid results with 6% revenue growth, 70 basis points of operating pretax margin expansion and 11% diluted operating earnings per share growth. And through the first 9 months, we generated about $8 billion of free cash flow, up 8% year-over-year. These results reflect strength across our portfolio and solid execution against our priorities.

Let me walk through the performance in each of our segments. Software revenue growth accelerated 4 points sequentially to 9% this quarter, including over 4 points of organic growth. This performance was driven by our high-value annual recurring revenue base, which grew 8% to $24.9 billion. Three of our 4 software subsegments grew revenue double digits, led by the combination of strong new business performance and pipeline closure rates, including the successful closure of around 2/3 of the large deals that slipped from the second quarter. Our results represent the high end of the range we outlined in July, reinforcing our confidence in the strength of our portfolio.

Turning to our segments. Red Hat revenue grew 11% again this quarter, fueled by bookings accelerating back to the high teens. Lightwell was recently made generally available, and we have strong early demand with a pipeline that is already approaching $700 million. OpenShift is now $2.4 billion ARR business, with growth accelerating to more than 30%. Our virtualization offering continues to scale with nearly $800 million in contracts signed since early 2024 and about $750 million in pipeline.

Data grew 20%, driven by another strong performance from Confluent with bookings growth of about 20%. Transaction processing grew 10% in the quarter, driven by demand for this mission-critical software and strong execution against our pipeline. HashiCorp continues to perform well, growing bookings across all geographies and accelerating revenue growth sequentially. However, automation was impacted by a tougher compare.

Turning to infrastructure. Revenue growth accelerated to 5% and driven by 32% growth in distributed infrastructure. Storage delivered record revenue growth and continued to gain market share with broad-based demand across flash, fusion, tape, in IBM Z storage. Strength across storage and power reflects the growing importance of differentiated infrastructure as clients modernize their environments, address data growth, and support AI at scale.

With the sizable backlog in distributed infrastructure exiting the quarter, we are well positioned for continued momentum in the fourth quarter. C-17 continues to perform exceptionally well, wrapping on the strongest start to a Z program last year. Program to date performance remains well ahead of the prior generation. up over $1 billion. And this quarter, we had the strongest midrange launch in IBM Z in nearly 2 decades. Beyond the strength of the program, IBM mainframe clients representing approximately 85% of installed MIPS capacity are maintaining or growing their capacity and aggregated installed capacity has tripled over the last few programs.

Consulting revenue grew 2% this quarter, reflecting continued client demand for AI-enabled transformation. Growth was led by strategy and technology, up 4% with strength in application modernization and data and AI transformation, while cybersecurity delivered nearly double-digit growth in intelligent operations. AI is increasingly embedded in broader transformations our clients are undertaking, representing about 30% of our consulting revenue in the quarter and 35% of our backlog.

We are also expanding the ecosystem of capabilities we bring to clients, including through our new strategic partnerships with OpenAI and our expanded relationship with Google Cloud as we help clients scale enterprise AI. Clients remain at different stages of adoption, but we continue to see them modernizing applications strengthening their data foundations and reengineering workflows as they move forward toward embedding AI into core business processes at scale. Our integrated model, combining deep domain expertise, technology and reusable assets, positions us to help clients capture business value from AI while accelerating time to value.

Let me now discuss profitability. Adjusted EBITDA and operating pretax margins expanded by 60 and 70 basis points, respectively. As I mentioned last quarter, we accelerated our productivity initiatives to help enhance our operating profit margin and free cash flow commitments while continuing to invest for growth. We made significant progress this quarter, driving efficiencies across G&A and sales, reinforcing the power of our productivity flywheel. Segment profit margins expanded by 210 basis points in software and 120 basis points in infrastructure driven by ongoing productivity actions.

Consulting segment profit margin declined modestly, as we reinvested productivity gains into digital assets, accelerating our transition towards a Services-as-Software model to capture the growing AI opportunity. Through the first 9 months of the year, we generated about $8 billion of free cash flow, up about $600 million year-over-year, driven by about $1 billion increase in adjusted EBITDA offset by higher CapEx, cash taxes and net interest expense.

Our free cash flow margin for the first 9 months of the year is the highest in reported history, highlighting our strong execution against productivity initiatives and laser focus on operational discipline. We exited the third quarter with a strong liquidity position and a solid investment-grade balance sheet with cash of $11.1 billion. We returned $4.8 billion to shareholders in the form of dividends through the first 9 months of the year. Our debt balance ending the quarter was $63.8 billion, including debt of $12.7 billion for our financing business, with the receivables portfolio that is almost 80% investment grade.

Now let me talk about what we are seeing going forward. The strength and diversity of our portfolio disciplined capital allocation and sustained productivity execution underpin our revenue growth, earnings expansion and free cash flow performance. Through the first 9 months of the year, we delivered over 4% revenue growth, 9% operating pretax income growth, 10% operating earnings per share growth and 8% free cash flow growth.

We continue to expect revenue growth for 2026 in the range of 4% to 5% and are raising our expectation for free cash flow growth to more than $1 billion. 90 days ago, we provided a full year revenue growth range with profit and free cash flow expectations anchored to a 4% base case. We continue to take the same approach for the full year, anchoring profit, free cash flow and segment expectations to that base case.

Given our strong third quarter software performance, our confidence in the full year outlook has increased. We now expect full year software growth of approximately 7% to 8%, with about 7% as the revised base case. For the full year, we expect double-digit growth in hybrid cloud, mid-teens growth in data and low single-digit growth in automation. For transaction processing, we are raising our expectations for the full year with growth expected to be flat.

Infrastructure growth accelerated in the third quarter with significantly stronger program to program performance of z17, underscoring the durability of the platform and the value it delivers to clients. Combined with continued strength in distributed infrastructure, we continue to expect low single-digit infrastructure revenue growth for the full year.

In Consulting, backlog quality and momentum in generative AI continue to support a modest acceleration to low to mid-single-digit revenue growth for the year. In the third quarter, our operational discipline and accelerated productivity initiatives allowed us to enhance margins and deliver free cash flow commitments while continuing to invest for growth. These actions include deploying AI and automation at scale across our company, reducing third-party spend, improving sales and marketing efficiency, using AI to drive more efficient software development, optimizing our supply chain and enhancing services delivery.

We continue to expect 10 basis points of operating pretax margin expansion for the full year, reflecting the flow-through from the third quarter earnings beat and continued operational discipline and our operating tax rate expectation for the year remains in the mid-teens.

For the full year, we are raising our expectation for free cash flow growth to more than $1 billion. The primary driver of this growth will be adjusted EBITDA, offset by higher CapEx, higher cash tax and net interest expense. Our free cash flow continues to be driven by growth in adjusted EBITDA and demonstrates consistent free cash flow realization of about 130% over the last few years.

Let me close by saying that our third quarter performance reinforces our confidence in our portfolio, the resilience of our business model and our outlook for the year. At the same time, we continue to invest in the technologies that will shape IBM's next chapter of growth. As Arvind mentioned, Quantum continues to advance across technology, ecosystem adoption and scale up.

While still early, the progress we're seeing from enterprise engagements to government validation reinforces our confidence in both our road map and the long-term opportunity ahead. We look forward to discussing in more detail at our Quantum Day in December.

Arvind and I are now happy to take questions. Olympia, let's get started.

Olympia McNerney

Thank you, Jim. Before we begin Q&A, I'd like to mention a couple of items. First, supplemental information is provided at the end of the presentation. And then second, as always, I'd ask you to refrain from multipart questions. Operator, let's please open it up for questions.

Operator

[Operator Instructions] And our first question comes from Amit Daryanani with Evercore.

Perguntas e respostas

Amit Daryanani

Yes. Great to see some nice software acceleration here. Arvind, I think last quarter, we discussed a lot about customers prioritizing infrastructure over software purchases. This quarter, it seems like transaction processing is at double-digit growth. while infrastructure has remained very strong for you folks. Can you just talk on what changed in customer spending priorities? Are you primarily just recovering deferred transaction at this point? Or are you actually starting to see infrastructure investments starting to create more incremental demand for IBM software. And with Red Hat bookings actually growing at high teens, is that an early indication that software can actually accelerate as you go into '27?

Arvind Krishna

So Amit, thanks for that question, and I also thank you for observing the numbers. Let me comment on the overall customer priorities, the tied towards infrastructure and Red Hat and I'm going to ask Jim to cover the transaction processing piece a little bit. So first, as we look at customer spending priorities, customer spending priorities in trying to leverage AI remains strong.

But as you begin to leverage it, how does the average customer get value. They have to rewrite an application, they have to modernize an existing application and you then ask the question, where do those run? That tends to be a tailwind for Red Hat OpenShift. And I think we are at the very beginning of this cycle. So I expect that to carry on for a long time.

Then the moment they get to saying, okay, I'll write the application on Red Hat OpenShift. You then get to how do I unlock the data from all of the enterprise data that I need to power that AI application. Remember, 90% of our enterprise data is not on the cloud. It's very much inside the enterprise. So Confluent tends to be a tailwind. I expect these to carry on for a long time. The automation portfolio will get a benefit when these applications all get to scale and people start worrying about managing hundreds and thousands of them, at that point, automation will also get a tailwind.

On the point you're making on Red Hat, look, we are very comfortable saying that based on what we can see, we are comfortable about double-digit growth in the fourth quarter of this year. Right now, we don't really want to comment on 27 growth. But generally speaking, I can say when we get these kinds of booking growth, it crosses into good revenue growth going forward. Jim, maybe a comment on TP.

James Kavanaugh

Yes. Just to build on your point, Arvind, and I'm going to thank you very much for the comment and the question. We're very pleased overall with our software performance team executed extremely well. Four points sequentially to 9% printed here in third quarter, and it was all organically driven, pervasively across our portfolio of 3 of our 4 subcategories accelerated and grew double digits overall. And that was a reflection of both. We talked a lot last quarter about the 80% high-value recurring revenue growing nicely at high single digit, but we saw a very nice rebound in our transactional business, that remaining 20% that grew actually double digits, led by our ELAs that were up north of 20% with an NRR of north of 130% overall.

That's a function of, yes, disciplined execution on slip deals but also a function of net new business contribution in the quarter. And I would tell you before all of you asked, about 1/4 of it is the slip deals with regards to overall software acceleration and about 3/4 of it is net new business that we are capturing overall, so to build on Arvind's point.

Now on TP. TP, we're obviously pleased. Double-digit growth I think this instantiates the demand for our mission-critical strategic mainframe software. A lot of discussions a quarter ago about demand destruction versus timing I think this puts us the rest right now because when you take a look at it, we've been talking all year long about the reinforcement of confidence we have in our mainframe platform. and how we monetize the strong z17 performance, right?

That model is the monetization model is the more capacity you put in the marketplace, the higher monetization opportunity the higher price opportunity, a higher value creation opportunity. Z17 today, install MIPS are up high teens, 6 quarters in. Ship MIPS are up over 100%. That is the future monetization opportunity that we feel extremely confident about as to the acceleration potential of TP in the future. So I appreciate the question and great place to start.

Operator

Your next question comes from Wamsi Mohan with Bank of America.

Wamsi Mohan

Thanks for the color you just shared on TP, but maybe if we could just double click on that. I mean, last quarter, you were thinking that TP might be down low to mid-single digits for the second half, and you called it a 27% growth vector it grew 10% instead. So maybe, Jim, as part of those like 1/4 of software upside really came from those push deals. Could you share some color more specifically with respect to TB, if that was similar or higher in relation to the deals that closed from those push deals?

And how many of those low tens of slip deals have not closed because you've not really changed your 4% to 5% for the year, but your commentary and your numbers would suggest that you're gravitating much more towards the higher end of that. if you could share any more incremental color. And on through-cycle TP performance, is that still something that we should think of as mid-single digit? That would be great.

Arvind Krishna

I know you addressed the question to Jim, but I'm going to start and then ask Jim to color -- give a lot of color on both the slip deals, the TP performance and the cycle-to-cycle compare, if I sort of paraphrase your question short. So let's just talk about TP. The questions everybody had last quarter was, does this imply that overall demand for mainframe has gone down. And we are very confident that this was a timing question, not a destruction question. And so at the macro level, as people deploy all of that hardware as they deploy that MIPS as the volumes of the underlying transactions increase, they need this mainframe software to support that mission-critical business. That's why we were confident it would come back.

Now to your point of us saying low to mid-single-digit decline, we want to be cautious and we want to be prudent. We want to see the absolute baseline and we don't want to call above it. I think what really happened was we got not only the deferral from 2Q, we got a normal 3Q buying cycle on mainframe. So kind of got it both together. I would be gated and tell you I don't expect double-digit performance in TP to keep going. But I would expect to see now more normalized performance, especially going into next year and perhaps moderation from 3Q going into the fourth quarter. But it speaks to the underlying demand for the mainframe around resilience, around the economics around the architecture that supports 8, 9s of availability.

Let me point it out 8, 9s of availability. But the transactions in the mainframe tend to be almost the opposite of what happens in Here, you need things that are completely deterministic, that are repeatable and that you can look at it with a financial color that they're meeting all the requirements of those kinds of transactions. And there's plenty of other things that will happen on distributed infrastructure. I think in terms of the numerics, I'm going to hand it to Jim to cover with you.

James Kavanaugh

Yes, Wamsi, thanks for the question overall. Let me just kind of reiterate a little bit about where I ended the last question to Amit. When you look at the health of our software business that accelerated 4 points quarter-to-quarter, it was a function of both the high-value recurring revenue. And again, the underpinnings of that is the Red Hat strength and acceleration. It's excellent start to our Confluent acquisition. It's record performances in Hashi overall, and that part of the business is doing well. The transactional where the big difference changed.

Last quarter, it impacted our software growth by 4 to 5 points when we were down double-digit transactional. This quarter, we were up double-digit transactional. And I'll remind you, that's 20% of our software book, annual book is about $32 billion, give or take. And that today, offers about 2/3 to 3/4 of that is ELAs. ELAs are strong incumbency positions that we've earned trust of our clients for decades around mainframe running their core architectures.

On top of that, we build a moat and we build a software distributed business model that captures that very attractive economic multiplier of $3 to $5 a software for every dollar we land on mainframe. Now within that, TP is disproportionate. But with that moat, we give clients flexibility on where they want to choose that ELA spend as to where it goes between data, between automation and between TP. Red Hat is not part of our ELAs overall. And what we saw here, Bolt is a contribution in the quarter on slipped deals. Let's put this in perspective.

We printed a number, what, $7.6 billion of software the slip deals were tens of slip deals. So let's kind of put that in a ballpark from 2Q of, what, $400 million, which is what we missed software buy. We closed 2/3 of those. That's $250-plus million. That is the 25% of the contribution and the growth that we've had of 4 points. By the way, that was disproportionate to TP. That's why you see well up at double digits overall. We didn't get a lot out of that in data and in automation and some of that should be fourth quarter.

Now fourth quarter, we gave a guide, I think, to use Arvind's word, anchored in our philosophy 90 days ago, prudent, cautious. We're anchoring this thing at the low end, approximately 7%. By the way, put that in perspective, I think the current Street estimates for the year are about $32 billion of revenue, we feel comfortable with that $32 billion of revenue. So that's kind of where we're at. I think I covered most of your questions.

Operator

Your next question comes from Ben Reitzes with Melius Research.

Benjamin Reitzes

I appreciate the question. I wanted to ask about your long term -- the short-term puts and takes we get. But at your latest Analyst Day, you laid out that you would approach double digits in software as a long-term target. -- based on what you're seeing, do you still feel confident in that? And how is M&A going to play a role in terms of achieving that? Because in order to hit it, I guess, you got to accelerate a point after this year. So a lot of folks are wondering where M&A plays in that.

James Kavanaugh

Thanks, Ben. I appreciate the question overall. Obviously, that's not going to surprise you. we'll spend a lot more time on this question here in January. We got the team extremely focused already. We're 21 days in arguably the most important quarter we have in the fourth quarter overall. But let me take a step back first around IBM. And then as Arvind and I have been talking here as we're heading into our planning cycle and laying out all of our investment and capital structure strategies for '27 talk a little bit about IBM then hit your question on software.

We've got a lot of confidence in our strategy and our portfolio is well positioned to deliver sustainable, durable growth on our model. we feel extremely confident in that. Why? We got an attractive TAM market backdrop of technology, the diversity of our portfolio, leveraging our hybrid cloud and AI platform-centric model, our continued portfolio optimization to shift to higher growth end markets, new innovation.

We continue to bring the market around Lightwell, around Gen AI and around to your question, M&A growth synergies all of that will play out. But from a segment perspective, since you asked about software, I'll give you some early indicators. One, we got a high-value recurring revenue stream that's been accelerating all year long $25 billion ARR book of business, high single-digit growth. We execute to what our guide is here. We should be approaching double digits here in the fourth quarter on that 80% high value, that's one critical indicator.

Two, new innovation we're bringing to market. We're extremely excited to Arvind's point, around Red Hat, the acceleration, the high teens growth. We've got $700 million worth of pipeline on Lightwell alone in front of us right now with $1 trillion TAM opportunity around open source vulnerabilities. We got virtualization, a tremendous opportunity. We got hybrid cloud, containerization opportunity. So we feel pretty good about the markets we're in, the investment in the portfolio that we have built up overall.

Three, we got 2027 as a year of growth synergies, right? 2026 was about integrating and getting confluent into our mode of operation and go-to-market and scale '27s around the growth synergies. And finally, to the TP question, we've got high teens growth in installed MIPS out in the marketplace. Last 5 years, our MIPS are up 2x. Last 10 years, our installed MIPS are up 10x. That is the monetization vehicle at TP, and Arvind talked about it, I talked about it. We think that's a growth acceleration back to our model overall. We'll quantify that in January, but that gives you a little bit of perspective of our early thinking about '27 on software and IBM.

Operator

The next question comes from Raimo Lenschow with Barclays.

Raimo Lenschow

Perfect. Two quick ones, if I may. First, congrats on the software performance. On data, 20% growth. Obviously, some of that is confident, but like what are you seeing there on upset across on Confluent. We have it for a few quarters now, but that looks like it's starting to click for you. So can you kind of double-click on that one? And then one for Jim. Gross margins came up with investors. Can you just give us a comment there, what drove the performance there?

Arvind Krishna

Raimo, thanks for the question, and thanks for noticing the data growth. If I look at Confluent, I actually want to broaden it. There is Confluent, which is important for moving data in real time, allows data to remain in place, but still be available for AI. But coupled to that are what I call lake houses, and we are very excited about the upcoming cross-sell opportunity with Streamhouse, which is also another way to store data that is sort of in flight.

So the Confluent piece unlocks your data, be it inside the enterprise, be it in an application, be it in a SaaS application, be it on a cloud, but still have it completely available for AI agents as well as inferencing -- really important capability, and you saw that growth pull through. But lake houses are also important as people are beginning to cleanse their data for leverage in AI, and we saw growth there also.

And then we're excited about what is going to come with the capability we are about to announce in a couple of weeks around the Stream house, which is also then going to be directly a cross-sell and upsell on top of Confluent. So unlocking data for AI, making sure that you don't have to create multiple copies because that creates a lot of extra cost, but still have it available as quickly as you need it is the fundamental capability we believe is going to be unlocked in the enterprise, leveraging all of these capabilities, and that contributes to the overall growth there.

Jimmy, I'll give it to you for the gross margin question.

James Kavanaugh

Raimo, thank you very much for noticing the operating leverage engine that we've been driving across this company. You look at what the last 4 years were probably up what, about 1,000 basis points of operating leverage across this company. By the way, that has delivered high-quality sustainable cash generation. Over the last 4 years, our cash is up roughly $7 billion of growth and 700 basis points of free cash flow margin on that 1,000 basis points of operating leverage.

But when you look underneath it, we continue to improve the structural competitiveness of this business model. We still believe we got a lot of headroom here. When you take a look at our profit, earnings up double digits, our operating margins are up 70 basis points. It's delivered by -- we are driving in the third quarter, 500 basis points of gross productivity in our model.

We've always said in our model, about 1/3 is for margin protection. About 2/3 enables us to drive that productivity flywheel where we reinvest back into the business around R&D, innovation, go-to-market model ecosystem to drive that flywheel of growth and competitive advantage. 500 basis points is well above what our model is overall. How are we doing that? We're running our playbook. That playbook, we believe, has headroom. It's reimagining everything between Arvind and myself about how we execute and run this company. And that gets into leveraging data, a whole new way of workflow reimagination embedding technology, digitization, AI into our company.

We've been talking about in prepared remarks, how do you reimagine a whole new way for software development? How do we reimagine a whole new way on go-to-market models? How do we redesign the G&A structure, the way we run our company. And I think there's a lot of headroom there. A few years in, we are now north of $5.5 billion of productivity out of this company. That is a great flywheel for investment flexibility and capability. So the most important thing is it provides that free cash flow generation engine, and it applies that investment flexibility so we can continue to compete both organically and inorganically to invest.

Operator

Your next question comes from Brent Thill with Jefferies.

Brent Thill

Arvind, I'm curious in some of the deals maybe early in the year, there was a little bit of an AI Brightlight. I don't know what to do. I need to kind of wait and investigate and maybe the investigation period went on and they realized maybe some assistants couldn't actually achieve what the claims were and I'm curious if you're maybe seeing some element of this and some of the deals recouping where the claims were big, but perhaps the reality wasn't matching the claim and you're able to deliver on that. Is that something you've seen? Or is that not a scenario that you've experienced?

Arvind Krishna

So Brent, let me make sure I parse the question correctly, but I'm going to sort of maybe answer it in my own words. I think what we are seeing is that at least larger enterprises are becoming a lot more cognizant and a lot more aware of what is the true ROI of all of their AI spend. They're also getting watered in some of the AI spending completely out of control with no real return.

But the way I've always characterized it is, there is no question to us, and this is the advice I gave every C-suite member that I meet that what we're trying to give people on AI, the productivity the advantages of AI, the way it's going to change work is very real. So let me not make any confusion around that. That said, a lot of people are underplaying how much work it is to integrate AI into your workflows to fundamentally sit back and say, how do I redesign the workflow to take advantage of AI. It's not a little RPA tool that you bolt on and you get something.

If you do not spend the time and energy to redesign your workflow, you're not going to get the full benefit. So if you're willing to do that, there's a huge benefit to be had. But that implies you've got to pick a few, you've got to learn how to scale them for full impact across the enterprise and don't do 100 little experiments you then do not have enough investment to get them to scale. That is what I think we are seeing. So I think what we're seeing Brent is now enterprises as they're getting mature as they're stopping the experimentation are saying, "All right, who can help me get through this bigger piece of workflow design as well as integration into our existing systems?" Because you're not likely going to throw out your ERP system. So how do you leverage it, but still get the benefits of AI and then they're picking their partners in terms of which software do they are they willing to trust to do this, which people have the knowledge to come do this with them as well as can be really efficient about a token spend as opposed to leave it completely uncontrolled.

So I agree, I think, with your thesis, but maybe a slight redirection in terms of why we're seeing that change. And yes, that is a tailwind for us. That is why I kind of repeat what I said at the beginning. That is my OpenShift, confluent, mainframe with fraud are all seeing the benefits of that play go on as we are proceeding down this path.

Operator

The next question comes from Fatima Boolani with Citi.

Fatima Boolani

I wanted to go back to the discussion around transaction processing simply because it was such a remarkable sequential recovery in that franchise. And Jim, I understand so much of that is piggybacking off of the ELA momentum that you regained in the quarter from the transactional side of the business? But I'm curious if you can help us unpack a little bit more on that strength.

I think you talked about a high teens growth in mix ship. So wondering if you could unpack and break down for us the quantity and shift mix growth in some of those slipped deals because those were existing customers. And to the extent you've been able to leverage a little bit of pricing power in this situation because I know that's not historically a lever that you've used. So we'd love to get a little bit more second order detail on that transaction processing and slope of recovery between the quantity and quality growth as well as pricing uplift?

James Kavanaugh

Thanks, Fatima. I appreciate the question. Let's double-click back down on TP. First, let me just reiterate what is the monetization model, right? TP is transaction processing that runs a mission-critical software on top of the most enduring platform that being mainframe. By the way, as we've talked about many times before, mainframe runs 70% of the industry's transaction volumes in terms of value. 45 of the top 50 banks, 4 to 5 top airlines, 7 of the top 10 retailers around the world. I think 75%, 78% last number I've seen the Fortune 100 run mainframe as a core architecture. .

So TP is the core mission-critical software that enables the most secure, reliable and scalable platform around. The way we monetize that value is the more capacity out there, the more opportunity to monetize value, the more opportunity to monetize price, the more opportunity to monetize value creation of TP. So when you take a look at our last 2, 3 cycles, we run 145 million MIPS around the world. Some of those are standard MPs on the platform. Some of those are new Linux specialty MIPS, new cloud-native applications, which is why the mainframe has been a growth vehicle over the last 2 to 3 cycles overall.

That $145 million MIPS are z17, 6 quarters in, 125%, the highest recorded program to date above z16, which was the prior highest program to date. We're up, I think, 18% ship MIPs through 6 quarters. We're up over 100% of ship mix. Why do I differentiate it? Read the first one is consumption, capacity and workload, we can monetize value immediately. The second is the opportunity. It's the TAM, right, and us driving workloads on that.

So when you take a look at it, that's what's been driving the step function change in growing meaningful over time. It was only a handful of years that our TP portfolio was down, what, mid-single digit, high single-digit growth. We then stabilize that growth about 4 years ago. And then through z16, we saw an acceleration, but I'll reiterate that z16, when you look at the launch year, it was about flat the launch year, then it kind of went up to about mid-single digits year plus 1 and then it went up to high single-digit year plus 2.

Right now, in z17, we were about flat launch year. We got different dynamics playing out right now. So we're about prudently cautious calling flat for the year, but what 100% ship maps and 18% install MIPS, I would say that's a pretty good future monetization opportunity in 2027 going forward.

Operator

Your next question comes from Brian Essex with JPMorgan.

Brian Essex

Congrats on the software acceleration from me as well. Arvind, I caught the prepared remarks, at least some of the news remarks that you had about fines deploying super intelligence in ways that transform entire operating models. And I appreciate that that's more than just increasing operating efficiency. But the question I have for you is as your customers assess those transformation efforts, how focused are they on sovereign AI? Is that becoming a more meaningful part of your conversations -- and how are you seeing your customers focus on the protection of their data and IP as they assess how they deploy AI for their transformation efforts?

Arvind Krishna

Brian, thank you first for the question. and thank you for noticing the precise words I did use in the prepared remarks. So to get to sovereign AI, sovereign AI is definitely becoming a much larger conversation. While we have talked about it for the last 1.5 years, I would not tell you that in '24, it had much traction. In '25, a few people began to pay some attention and in '26, it is now beginning to pick up. And let me explain for everybody else exactly what we mean by that. If you go to use a large public LLM, effectively, while they're certainly not stealing your data, though they're implicitly maybe learning what kind of questions are asked. There are traces left in the underlying logs and so on. That are not full, but that do give some indication of what all it is being used for as well as some elements of the data that is being used.

So sovereign becomes how do you make sure that none of that happens -- so people are turning now to 2 kinds of answers. That is why I think there is such a big boom in AI infrastructure on-premise, and we can see some of the companies that benefit from that. But the so will we in our mainframe with our Spyre cards as time goes on. The other part of this is what is driving the boom behind open weight models because there you have the permission and the IP rights to run them on either on-premise infrastructure or in private clouds, where the enterprise controls what is happening to the underlying logs and data.

That is definitely what is happening there, and we can see that and the boom behind some of the companies that are creating open wave models is also increasing behind that. I'll reflect on a conversation I had with a large bank CEO. In this case, it was outside the U.S., but it's a very large and friendly country. And their view was, look, we'll do some early experiments on the larger frontier LLMs. But for what we're doing inside the bank, we need to run it in a way that is sovereign, meaning nobody can take any of the underlying data on underlying logs that are there.

I was talking to somebody who does trading and thereon -- we are not going to expose our underlying proprietary models, anything outside, but we need to run this in a sovereign way. I believe this is going to turn into a tailwind for many players in the industry certainly, the infrastructure in semiconductor players, but also for those of us who help enterprises deploy in a secure and in a private way.

Operator

And our last question comes from Matt Swanson with RBC Capital Markets.

Matthew Swanson

Great. And maybe this is just kind of a natural follow-up to what Brent was asking. But you had mentioned earlier that what AI costs and who controls it have kind of been like the 2 primary questions that I'm probably to start off with you. Who controls it obviously fits right into that sovereign AI conversation. But could you just kind of talk a little bit about how people are addressing the trade-offs if there are trade-offs between those 2 segments and how they're architecturing their networks in order to maximize for both those things? And just how kind of like symbiotic they can be?

Arvind Krishna

So Matt, thanks for that question. And also allowing me to give bit more detail on this topic. If I go back to late '24, the difference between the most advanced model, which the technology is mostly a frontier LLM model and alternate models was a 2:1 or 3:1 gap, meaning if you ran some assessment, some benchmark, and it was broad-based, not narrow, the top model might score a 70 and open model might score a 20 or a 30. You kind of react with -- that's a lot of capability we're giving up. And we all know that performance, in some sense, outcome for the enterprise is highly dependent upon that. That's too big a gap. Nobody is going to accept that.

In late '25, we began to see that gap shrink where maybe the gap of not 70 to 20 or 30, it was 70 to 50. But when you looked at those curves and you looked at the improvement, and you looked upon the sequential improvement, we were very comfortable that we get is to probably 100 for the top and 90 for the alternate model. So now the question becomes, how do you do that?

Now the question that the enterprise struggles with is where are my kernel engineers, where are the people who worry about the underlying network, who can make sure that the model is clustered or GPUs that are close to each other in network proximity. -- who can make sure that you're doing the right cashing of results so you get an incredible response. I'm telling you that, that gap is probably still there, except for the most sophisticated enterprise.

So they kind of go to -- all right, I got it that the answer is 4 more expensive, but it's an easy answer. As those of us who work in this world produce much more sort of easy button software that can give you that capability for the alternate models. And I would say it's not a huge amount of infrastructure. Our competition is that for an average large enterprise, you can get going with maybe 2 or 3 racks which is not a huge amount of infrastructure, but it gives you the ability to run those open rate models currently anywhere from 25% to 10% of the token cost of the frontier model -- that is very attractive.

Given that they are now 90% as effective. It's a simple question for any enterprise. Would you like something that's 10% of the cost, if it's 90% as good as the best, -- or do you want to pay that 10x higher cost? And then the question is, how many tarts require that. I would tell you some do, but the vast majority, summarizing an e-mail, examining a contract, answering a customer call composing an e-mail, all of those can be done as well with the multiple answer, and that is what is going to drive a lot of this.

So Matt and everybody else, thank you for those questions. Look, we are pleased with our performance this quarter and all our segments accelerated growth. Our portfolio strength, disciplined execution and relentless focus on productivity, reinforce my confidence in our trajectory. I look forward to sharing our progress with you as we close out the year, and we'll talk to you in January about that.

Olympia McNerney

Thank you, Arvind. Operator, let me turn it back to you to close out the call.

Operator

Thank you for participating on today's call. The conference has now ended. You may disconnect at this time.