Q1 2026 Broadcom Inc Earnings Call

March 4, 2026

Speaker #1: To welcome to Broadcom Inc.'s first quarter fiscal year 2026 financial results conference call. At this time, for opening remarks and introductions, I would like to turn the call over to GU, Head of Investor Relations of Broadcom Inc.

Operator: Welcome to Broadcom Inc.'s Q1 fiscal year 2026 Financial Results Conference Call. At this time, for opening remarks and introductions, I would like to turn the call over to Ji Yoo, Head of Investor Relations, Broadcom Inc.

Speaker #2: Thank you, Operator, and good afternoon, everyone. Joining me on today's call are Hock Tan, President and CEO; Kirsten Spears, Chief Financial Officer; Charlie Kawaz, President, Semiconductor Solutions Group; and Ram Vilaga, President, Infrastructure Software Group.

Ji Yoo: Thank you, operator, and good afternoon, everyone. Joining me on today's call are Hock Tan, President and CEO, Kirsten Spears, Chief Financial Officer, Charlie Kawwas, President, Semiconductor Solutions Group, and Ram Velaga, President, Infrastructure Software Group. Broadcom distributed a press release and financial tables after the market closed describing our financial performance for Q1 fiscal year 2026. If you did not receive a copy, you may obtain the information from the investor section of Broadcom's website at broadcom.com. This conference call is being webcast live, and an audio replay of the call can be accessed for one year through the investors section of Broadcom's website. During the prepared comments, Hock and Kirsten will be providing details of our Q1 fiscal year 2026 results, guidance for our Q2 of fiscal year 2026, as well as commentary regarding the business environment.

Speaker #2: Broadcom distributed a press release and financial tables after the market closed describing our financial performance for the first quarter fiscal year 2026. If you did not receive a copy, you may obtain the information from the investor section of Broadcom's website at broadcom.com.

Ji Yoo: This conference call is being webcast live, and an audio replay of the call can be accessed for one year through the investors section of Broadcom's website. During the prepared comments, Hock and Kirsten will be providing details of our Q1 fiscal year 2026 results, guidance for our Q2 of fiscal year 2026, as well as commentary regarding the business environment.

Speaker #2: This conference call is being webcast live and an audio replay of the call can investors section of Broadcom's website. During the prepared comments, Hock and Kirsten will be providing details of our first quarter fiscal year 2026 results, guidance for our second quarter of fiscal year 2026, as well as commentary regarding the business environment.

Speaker #2: We'll take questions after the end of our prepared comments. Please refer to our press release today and our recent filings with the SEC for information on the specific risk factors that could cause our actual results to differ materially from the forward-looking statements made on this call.

Ji Yoo: We'll take questions after the end of our prepared comments. Please refer to our press release today and our recent filings with the SEC for information on the specific risk factors that could cause our actual results to differ materially from the forward-looking statements made on this call. In addition to U.S. GAAP reporting, Broadcom reports certain financial measures on a non-GAAP basis. A reconciliation between GAAP and non-GAAP measures is included in the table attached to today's press release. Comments made during today's call will primarily refer to our non-GAAP financial results. I will now turn the call over to Hock.

Speaker #2: In addition to U.S. GAAP reporting, Broadcom reports certain financial measures on a non-GAAP basis. A reconciliation between GAAP and non-GAAP measures is included in the tables attached to today's press release.

Ji Yoo: A reconciliation between GAAP and non-GAAP measures is included in the table attached to today's press release. Comments made during today's call will primarily refer to our non-GAAP financial results. I will now turn the call over to Hock.

Speaker #2: Comments made during today's call will primarily refer to our non-GAAP financial results. I will now turn the call over to Hock.

Speaker #3: Thank you, G. And thank you, everyone, for joining us today. In our fiscal Q1 2026 total revenue reached a record $19.3 billion, and that's up 29% year on year.

Hock Tan: Thank you, Chi, and thank you everyone for joining us today. In our fiscal Q1 2026, total revenue reached a record $19.3 billion, and that's up 29% year-on-year and exceeding our guidance on the back of better than expected growth in AI semiconductors. This top-line strength translated into exceptional profitability with Q1 consolidated adjusted EBITDA hitting a record $13.1 billion, which is 68% of revenue. These figures demonstrate that our scale continues to drive significant operating leverage. We expect this momentum to accelerate as our custom AI XPUs hit their next phase of deployment among our five customers. Looking ahead to next quarter, Q2 2026, we're guiding for consolidated revenue of approximately $22 billion, which represents 47% year-on-year growth. Let me now give you more color on our semiconductor business.

Speaker #3: And exceeding our guidance on the back of better-than-expected growth in AI semiconductors. This top-line strength translated into exceptional profitability with Q1 consolidated adjusted EBITDA hitting a record $13.1 billion, which is 68% of revenue.

Hock Tan: These figures demonstrate that our scale continues to drive significant operating leverage. We expect this momentum to accelerate as our custom AI XPUs hit their next phase of deployment among our five customers. Looking ahead to next quarter, Q2 2026, we're guiding for consolidated revenue of approximately $22 billion, which represents 47% year-on-year growth. Let me now give you more color on our semiconductor business.

Speaker #3: These figures demonstrate that our scale continues to drive significant operating leverage. Now, we expect this momentum to accelerate as our custom AI XPUs hit their next phase of deployment among our five customers.

Speaker #3: So, looking ahead to next quarter, Q2 2026, we're guiding for consolidated revenue of approximately

Speaker #1: $22 billion , which represents 47% year on year growth Let me now give you more color on our semiconductor business in Q1 , revenue was a record 12.5 billion .

Hock Tan: In Q1, revenue was a record $12.5 billion, as year-on-year growth accelerated to 52%. This robust growth was driven by AI semiconductor revenue, which grew 106% year-on-year to $8.4 billion, way above our outlook. In Q2, this momentum accelerates. We expect semiconductor revenue to be $14.8 billion, up 76% year-on-year. Driving this is AI revenue growth, which will accelerate very sharply to 140% year-on-year to $10.7 billion. Now, our custom accelerator business grew 140% year-on-year in Q1. This momentum continues in Q2. The ramp of custom AI accelerators across all our five customers is progressing very well. For Google, we continue our trajectory of growth in 2026 with strong demand for the seventh generation AI TPU.

Speaker #1: As year on year growth accelerated to 52% . This robust growth was driven by AI , semiconductor revenue , which grew 106% year on year to 8.4 billion , way above our outlook in Q2 This momentum accelerates and we expect semiconductor revenue to be 14.8 billion , up 76% year on year .

Speaker #1: Driving . This is AI revenue growth , which will accelerate very sharply to 140% year on year to $10.7 billion . Now , our custom accelerated business grew 140% year on year in Q1 .

Speaker #1: This momentum continues in Q2 . The ramp of custom AI accelerated accelerators across all our five customers is progressing very well for Google .

Hock Tan: The ramp of custom AI accelerators across all our five customers is progressing very well. For Google, we continue our trajectory of growth in 2026 with strong demand for the seventh generation AI TPU.

Speaker #1: We continue our trajectory of growth in 26 with strong demand for the seventh generation Ironwood TPU In 2027 and beyond , we expect to see even stronger demand from next generations of TPU for anthropic , we are off to a very good start in 2026 for one gigawatt of TPU compute and for 27 , this demand is expected to surge in excess of three gigawatts of compute Our Expo franchise I should add , extends beyond TPUs .

Hock Tan: In 2027 and beyond, we expect to see even stronger demand from next generations of TPU. For Anthropic, we are off to a very good start in 2026 for 1 gigawatt of TPU compute. For 2027, this demand is expected to surge in excess of 3 gigawatts of compute. Our XPU franchise, I should add, extends beyond TPUs. Now, contrary to recent analyst reports, Meta's custom accelerator MTIA roadmap is alive and well. We're shipping now, and in fact, for the next generation XPUs, we will scale to multiple gigawatts in 2027 and beyond. Rounding off for customers four and five, we see strong shipments this year and which we expect to more than double in 2027. We also now have a sixth customer. We expect OpenAI deploying in volume their first generation XPU in 2027 at over 1 gigawatt of compute capacity.

Speaker #1: Now , contrary to recent analyst reports , matters custom accelerator media roadmap is alive and well . We're shipping now and in fact , for the next generation Xbox , we will scale to multiple gigawatts in 27 and beyond Rounding off for customers four and five , we see strong shipments .

Hock Tan: Rounding off for customers four and five, we see strong shipments this year and which we expect to more than double in 2027. We also now have a sixth customer. We expect OpenAI deploying in volume their first generation XPU in 2027 at over 1 gigawatt of compute capacity.

Speaker #1: This year and which we expect to more than double in 2027 . We also now have a sixth customer . We expect OpenAI in volume , their first generation xpu in 2027 at over one gigawatt of compute capacity Let me take a second to emphasize our collaboration with these six customers to develop AI expertise is deep , strategic and multi-year .

Hock Tan: Let me take a second to emphasize our collaboration with these six customers to develop AI XPUs is deep, strategic, and multi-year. We bring to the partnerships, each of them unmatched technology in service, silicon design, process technology, advanced packaging, and networking to enable each of these customers to achieve optimal performance for their differentiated LLM workloads. We have the track record to deliver these XPUs at high volumes at an accelerated time to market with very high yields. Beyond technology, we provide multiyear supply agreements as our customers scale up deployment of their compute infrastructure. Our ability to assure supply in these times of constrained capacity in leading-edge wafers, in high bandwidth memory, and substrates ensures the durability of our partnerships. We have fully secured capacity of these components for 2026 through 2028. Consistent now with the strong outlook for our XPUs, demand for AI networking is accelerating.

Speaker #1: We bring to the partnerships , each of them unmatched technology in service . Silicon design process , technology , advanced packaging and networking to enable each of these customers to achieve optimal performance for their differentiated LM workloads , we have the track record to deliver these exposures and high volumes .

Speaker #1: At an accelerated time to market with very high yields and beyond technology , we provide multi-year supply agreements as our customers scale up deployment of their compute infrastructure .

Hock Tan: Beyond technology, we provide multiyear supply agreements as our customers scale up deployment of their compute infrastructure. Our ability to assure supply in these times of constrained capacity in leading-edge wafers, in high bandwidth memory, and substrates ensures the durability of our partnerships. We have fully secured capacity of these components for 2026 through 2028. Consistent now with the strong outlook for our XPUs, demand for AI networking is accelerating.

Speaker #1: Our ability to assure supply in these times of constrained capacity in leading-edge wafers, in high bandwidth memory, and substrates ensures the durability of our partnerships.

Speaker #1: And we have fully secured capacity of these components for 26 through 28 . Consistent with the strong outlook for excuse demand for AI , networking is accelerating .

Speaker #1: Q1 AI networking revenue grew 60% year on year and represented one third of total AI revenue in Q2 . We project AI networking to accelerate a lot more and grow to 40% of total AI revenue .

Hock Tan: Q1 AI networking revenue grew 60% year-on-year and represented 1/3 of total AI revenue. In Q2, we project AI networking to accelerate a lot more and grow to 40% of total AI revenue. We are clearly gaining share in networking. Let me explain. In scale out, our first-to-market Tomahawk 6 switch at 100 Tb per second, as well as our 200G SerDes are capturing demand from hyperscalers, whether they use XPUs or GPUs this year. This lead will extend in 2027 with our next generation Tomahawk 7, featuring double the performance. Meanwhile, in scale up, as cluster sizes at our customers expand, we are uniquely positioned to enable these customers to stay on direct attached copper through our 200G SerDes.

Speaker #1: We are clearly gaining share in networking Let me explain . In scale out . Our first to market , Thomas six switch at 100 terabits per second , as well as our 200 G30s are capturing demand from high hyperscalers , whether they use Spus or GPUs .

Speaker #1: This year . This lead will extend into 27 with our next generation Tomahawk seven featuring double the performance Meanwhile , in scaled up as cluster sizes and are at our customers expand , we are uniquely positioned to enable these customers to stay on direct attached copper through our 200 G service .

Hock Tan: This lead will extend in 2027 with our next generation Tomahawk 7, featuring double the performance. Meanwhile, in scale up, as cluster sizes at our customers expand, we are uniquely positioned to enable these customers to stay on direct attached copper through our 200G SerDes.

Speaker #1: As we next step up to 430 . In 2028 , our customers will likely continue to stay on direct attach copper , and this is a huge advantage as the alternative of going to optical is more expensive and requires significantly more power , reflecting the foregoing factors Our visibility in 2027 has dramatically improved today .

Hock Tan: As we next step up to 400 Gb SerDes in 2028, our XPU customers will likely continue to stay on direct attached copper. This is a huge advantage as the alternative of going to optical is more expensive and requires significantly more power. Reflecting the foregoing factors, our visibility in 2027 has dramatically improved. Today, in fact, we have line of sight to achieve AI revenue from chips, just chips in excess of $100 billion in 2027. We have also secured the supply chain required to achieve this. Now, turning to non-AI semiconductors. Q1 revenue of $4.1 billion was flat year-over-year in line with guidance. Enterprise networking, broadband, server storage revenues were up year-over-year, offset by a seasonal decline in wireless.

Speaker #1: In fact , we have line of sight to achieve AI revenue for chips , just chips in excess of 100,000,000,000 in 2027 . We have also secured the supply chain required to achieve this Now turning to non AI semiconductors Q1 revenue of 4.1 billion was flat year on year , in line with guidance Enterprise networking , broadband service , storage revenues were up year on year , offset by seasonal decline in wireless in Q2 .

Hock Tan: We have also secured the supply chain required to achieve this. Now, turning to non-AI semiconductors. Q1 revenue of $4.1 billion was flat year-over-year in line with guidance. Enterprise networking, broadband, server storage revenues were up year-over-year, offset by a seasonal decline in wireless.

Hock Tan: In Q2, we forecast non-AI semiconductor revenue to be approximately $4.1 billion, up 4% from a year ago. Let me now talk about our infrastructure software segment. Q1 infrastructure software revenue of $6.8 billion was in line with our guidance. It's up 1% year-on-year. For Q2, we forecast infrastructure software revenue to be approximately $7.2 billion, up 9% year-on-year. VMware revenue grew 13% year-on-year. Bookings continued to be strong and total contract value booked in Q1 exceeded $9.2 billion, sustaining an ARR annual, which is annual recurring revenue growth of 19% year-upon-year. Let me reinforce that this growth in our infrastructure software business reflects our focus in investments in foundational infrastructure and our infrastructure software is not disrupted by AI.

Speaker #1: We forecast non AI semiconductor revenue to be approximately 4.1 billion , up 4% from a year ago . Let me now talk about our infrastructure software segment .

Speaker #1: Q1 infrastructure software revenue of $6.8 billion was in line with our guidance , up 1% year on year for Q2 . We focused infrastructure , software revenue to be approximately 7.2 billion , up 9% year on year .

Hock Tan: VMware revenue grew 13% year-on-year. Bookings continued to be strong and total contract value booked in Q1 exceeded $9.2 billion, sustaining an ARR annual, which is annual recurring revenue growth of 19% year-upon-year. Let me reinforce that this growth in our infrastructure software business reflects our focus in investments in foundational infrastructure and our infrastructure software is not disrupted by AI.

Speaker #1: VMware revenue grew 13% year on year . Bookings continued to be strong and total contract value booked in Q1 exceeded $9.2 billion , sustaining an AR annual , which is an annual recurring revenue growth of 19% year upon year .

Speaker #1: Let me reinforce that this growth in our infrastructure software business reflects our focus and investments in foundational infrastructure and our infrastructure software is not disrupted by AI .

Speaker #1: In fact , VMware cloud Foundation , VCs is the essential software layer in data centers , integrating CPUs , GPUs , storage and networking into a common high performance private cloud environment .

Hock Tan: In fact, VMware Cloud Foundation, VCF, is the essential software layer in data centers integrating CPUs, GPUs, storage and networking into a common high-performance private cloud environment. As the permanent abstraction layer between AI software and physical chips, silicon, VCF cannot be disintermediated or replaced. It allows enterprises, in fact, to scale complex generative AI workloads effectively with agility that hardware alone cannot provide. We are confident that the growth in generative and agentic AI will create the need for more VMware, not less. In summary, let me put it all together for Q2 2026. We expect consolidated revenue growth to accelerate to 47% year-on-year and reach approximately $22 billion, and we expect adjusted EBITDA to be approximately 68% of revenue. With that, let me turn the call over to Kirsten.

Speaker #1: As the permanent abstraction layer between AI software and physical chips , silicon , DCF cannot be disintermediated replaced . It allows enterprises , in fact , to scale complex generative AI workloads effectively with agility that hardware alone cannot provide .

Speaker #1: We are confident that the growth in January and AI will create the need for more VMware, not less so. In summary, let me put it all together for Q2 2026.

Hock Tan: We are confident that the growth in generative and agentic AI will create the need for more VMware, not less. In summary, let me put it all together for Q2 2026. We expect consolidated revenue growth to accelerate to 47% year-on-year and reach approximately $22 billion, and we expect adjusted EBITDA to be approximately 68% of revenue. With that, let me turn the call over to Kirsten.

Speaker #1: We expect consolidated revenue growth to accelerate to 47% year on year and reach approximately $22 billion . And we expect adjusted EBITDA to be approximately 68% of revenue .

Speaker #1: So with that, let me turn the call over to Kirsten.

Speaker #2: Thank you . Hawk . Let me now provide additional detail on our Q1 financial performance . Consolidated revenue was a record $19.3 billion for the quarter , up 29% from a year ago Gross margin was 77% of revenue in the quarter .

Kirsten Spears: Thank you, Hock. Let me now provide additional detail on our Q1 financial performance. Consolidated revenue was a record $19.3 billion for the quarter, up 29% from a year ago. Gross margin was 77% of revenue in the quarter. Consolidated operating expenses were $2 billion, of which $1.5 billion was R&D. Q1 operating income was a record $12.8 billion, up 31% from a year ago. Operating margin increased 50 basis points year-over-year to 66.4% on favorable operating leverage. Adjusted EBITDA of $13.1 billion or 68% of revenue was above our guidance of 67%. Let's go into detail for our two segments. Starting with Semiconductors. Revenue for our Semiconductor Solutions segment was a record $12.5 billion, with growth accelerating to 52% year-on-year, driven by AI.

Speaker #2: Consolidated operating expenses were 2 billion , of which 1.5 billion was R&D . Q1 operating income was a record $12.8 billion , up 31% from a year ago .

Speaker #2: Operating margin increased 50 basis points year over year to 66.4% . On favorable operating leverage . Adjusted EBITDA of 13.1 billion , or 68% of revenue , was above our guidance of 67% .

Kirsten Spears: Operating margin increased 50 basis points year-over-year to 66.4% on favorable operating leverage. Adjusted EBITDA of $13.1 billion or 68% of revenue was above our guidance of 67%. Let's go into detail for our two segments. Starting with Semiconductors. Revenue for our Semiconductor Solutions segment was a record $12.5 billion, with growth accelerating to 52% year-on-year, driven by AI.

Speaker #2: Now , let's go into detail for our two segments , starting with semiconductors revenue for our semiconductor solutions segment was a record 12.5 billion , with growth accelerating to 52% year on year , driven by AI semiconductor revenue represented 65% of total revenue in the quarter .

Kirsten Spears: Semiconductor revenue represented 65% of total revenue in the quarter. Gross margin for our Semiconductor Solutions Group was up 30 basis points year-on-year to approximately 68%. Operating expenses of $1.1 billion reflected increased investment in R&D for leading-edge AI semiconductors and represented 8% of revenue. Semiconductor operating margin of 60% was up 260 basis points year-on-year, reflecting strong operating leverage. Now moving on to Infrastructure Software Group. Revenue for Infrastructure Software Group of $6.8 billion was up 1% year-on-year and represented 35% of revenue. Gross margin for Infrastructure Software Group was 93% in the quarter, and operating expenses were $979 million in the quarter. Q1 software operating margin was up 190 basis points year-on-year to 78%. Moving on to cash flow.

Speaker #2: Gross margin for our semiconductor solutions segment was up 30 basis points year on year to approximately 68% . Operating expenses of 1.1 billion , reflected increased investment in R&D for leading edge AI , semiconductors and represented 8% of revenue .

Speaker #2: Semiconductor operating margin of 60% was up 260 basis points year on year , reflecting strong operating leverage . Now moving on to infrastructure software revenue for infrastructure software of 6.8 billion was up 1% year on year .

Kirsten Spears: Now moving on to Infrastructure Software Group. Revenue for Infrastructure Software Group of $6.8 billion was up 1% year-on-year and represented 35% of revenue. Gross margin for Infrastructure Software Group was 93% in the quarter, and operating expenses were $979 million in the quarter. Q1 software operating margin was up 190 basis points year-on-year to 78%. Moving on to cash flow.

Speaker #2: And represented 35% of revenue Gross margin for infrastructure software was 93% in the quarter , and operating expenses were 979 million . In the quarter .

Speaker #2: Q1 software , operating margin was up 190 basis points year on year to 78% . Moving on to cash flow , free cash flow in the quarter was 8 billion and represented 41% of revenue .

Kirsten Spears: Free cash flow in Q1 was $8 billion and represented 41% of revenue. We spent $250 million on capital expenditures. We ended Q1 with inventory of $3 billion as we continue to secure components to support strong AI demand. Our days of inventory on hand were 68 days in Q1 compared to 58 days in Q4 in anticipation of accelerating AI semiconductor growth. Turning to capital allocation. In Q1, we paid stockholders $3.1 billion of cash dividends based on a quarterly common stock cash dividend of $0.65 per share. During Q1, we repurchased $7.8 billion or approximately 23 million shares of common stock. In total, in Q1, we returned $10.9 billion to shareholders through dividends and share repurchases.

Speaker #2: We spent 250 million on capital expenditures . We ended the first quarter with inventory of 3 billion . As we continue to secure components to support strong AI demand , our days of inventory on hand were 68 days in Q1 , compared to 58 days in Q4 in anticipation of accelerating AI semiconductor growth .

Speaker #2: Turning to capital allocation in Q1 , we paid stockholders 3.1 billion of cash dividends based on a quarterly common stock cash dividend of $0.65 per share during the quarter , we repurchased 7.8 billion , or approximately 23 million shares of common stock in total in Q1 , we returned 10.9 billion to shareholders through dividends and share repurchases .

Kirsten Spears: Turning to capital allocation. In Q1, we paid stockholders $3.1 billion of cash dividends based on a quarterly common stock cash dividend of $0.65 per share. During Q1, we repurchased $7.8 billion or approximately 23 million shares of common stock. In total, in Q1, we returned $10.9 billion to shareholders through dividends and share repurchases.

Speaker #2: In Q2, we expect the non-GAAP diluted share count to be approximately 4.94 billion shares, excluding the impact of potential share repurchases.

Kirsten Spears: In Q2, we expect the non-GAAP diluted share count to be approximately 4.94 billion shares, excluding the impact of potential share repurchases. We ended Q1 with $14.2 billion of cash. Today, we are announcing our board of directors has authorized an additional $10 billion for our share repurchase program effective through the end of calendar year 2026. Now moving on to guidance. Our guidance for Q2 is for consolidated revenue of $22 billion, up 47% year-on-year. We forecast semiconductor revenue of approximately $14.8 billion, up 76% year-on-year. Within this, we expect Q2 AI semiconductor revenue of $10.7 billion, up approximately 140% year-on-year. We expect infrastructure software revenue of approximately $7.2 billion, up 9% year-on-year.

Speaker #2: We ended the first quarter with $14.2 billion of cash. Today, we are announcing our board of directors has authorized an additional $10 billion for our share repurchase program, effective through the end of calendar year 2026.

Speaker #2: Now , moving on to guidance . Our guidance for Q2 is for consolidated revenue of $22 billion , up 47% year on year .

Kirsten Spears: We forecast semiconductor revenue of approximately $14.8 billion, up 76% year-on-year. Within this, we expect Q2 AI semiconductor revenue of $10.7 billion, up approximately 140% year-on-year. We expect infrastructure software revenue of approximately $7.2 billion, up 9% year-on-year.

Speaker #2: We forecast semiconductor revenue of approximately $14.8 billion, up 76% year on year. Within this, we expect Q2 AI semiconductor revenue to be up approximately 140% year on year.

Speaker #2: We expect infrastructure , software revenue of approximately 7.2 billion , up 9% year on year . For your modeling purposes , we expect consolidated gross margin to be flat sequentially at 77% .

Kirsten Spears: For your modeling purposes, we expect consolidated gross margin to be flat sequentially at 77%. We expect Q2 adjusted EBITDA to be approximately 68%. We expect the non-GAAP tax rate for Q2 in fiscal year 2026 to be approximately 16.5% due to the impact of the global minimum tax and the geographic mix of income compared to that of fiscal year 2025. That concludes my prepared remarks. Operator, please open up the call for questions.

Speaker #2: We expect Q2 adjusted EBITDA to be approximately 68%. We expect the non-GAAP tax rate for Q2 and fiscal year 2026 to be approximately 16.5%, due to the impact of the global minimum tax and the geographic mix of income compared to that of fiscal year 2025.

Speaker #2: That concludes my prepared remarks . Operator . open up the call for questions .

Speaker #3: Thank you To ask a question , you will need to press star one one on your telephone to withdraw your question . Press star one one again due to time restraints .

Operator: Thank you. To ask a question, you will need to press star one one on your telephone. To withdraw your question, press star one one again. Due to time restraints, we ask that you please limit yourself to one question. Please stand by while we compile the Q&A roster. Our first question will come from the line of Blayne Curtis with Jefferies. Your line is open.

Speaker #3: We ask that you please limit yourself to one question. Please stand by while we compile the Q&A roster. And our first question will come from the line of Blayne Curtis with Jefferies.

Speaker #3: Your line is open

Speaker #4: Hey . Good afternoon . Thanks for taking my question . It's just a clarification . Then the question just clarification on the greater than $100 billion .

Blayne Curtis: Hey, good afternoon, and thanks for taking my question. It's just a clarification, then the question. Just clarification, Hock, on the greater than $100 billion. I think you said AI chips. I just want to make sure you're clarifying the difference between the ASICs and networking and didn't know how rack revenue fits in there. The question, you know, I think the biggest overhang on the group here is that, you know, you grew roughly double in the quarter AI. I think that's what, you know, kind of cloud CapEx is growing this year. I'm just kind of curious your perspective, you know, I think given the outlook that you have for 2027, you should be a share gainer.

Speaker #4: I think you said AI chips. I just want to make sure you’re clarifying the difference between the ASICs and networking, and I didn’t know how racks revenue fits in there.

Speaker #4: And then the question I think the biggest overhang on the group here is that , you know , you grew roughly double in the quarter .

Blayne Curtis: The question, you know, I think the biggest overhang on the group here is that, you know, you grew roughly double in the quarter AI. I think that's what, you know, kind of cloud CapEx is growing this year. I'm just kind of curious your perspective, you know, I think given the outlook that you have for 2027, you should be a share gainer.

Speaker #4: AI I think that's what kind of cloud CapEx is growing this year . I'm just kind curious . Your perspective . You know , I think given the outlook that you have for 27 , you should be a share gainer .

Speaker #4: I'm just kind of curious. Your perspective in terms of the pessimism that investors kind of think of, that the hyperscalers need to get a return on investment in this year or next year, or if not, the year after.

Blayne Curtis: I'm just kind of curious your perspective in terms of the pessimism that investors kind of think of that the hyperscalers need to get a return on investment in this year or next year, or if not the year after. I'm just kind of curious your perspective, how you factor that into your outlook.

Speaker #4: I'm just kind of curious , your perspective on how you factor that into your outlook ?

Speaker #1: Well , what we see , what we have seen over the last few months and continue to see even more , is and it's really not so much talking about hyperscalers are our customers blame is limited to those few players out there , and some of them are hyperscalers .

Hock Tan: Well, what we've seen over the last few months and continue to see even more is, and it's really not so much talking about hyperscalers. Our customers, Blayne, is limited to those few players out there, and some of them are hyperscalers, some of them are not hyperscalers, but they all have one thing in common, which is to create LLMs, productize it, and generate platforms. Be it for enterprise consumption in code assistance of agentic AI, or be it for consumer subscription that we know about. Whatever it is that few prospects, and many of whom are customers now, who are creating this, Whether it's generative AI, agentic AI, but creating a platform. That's our customer. With respect to each of those guys, we are seeing very stronger and stronger demand for compute capacity.

Speaker #1: Some of them are not hyperscalers , but they all have one thing in common , which is to create LMS productize . It and generate platforms , be it for enterprise consumption in code assistance or or agentic AI , or be it for consumer subscription that we know about whatever it is is that few prospects and many of whom are customers now , who are creating this , are a general , whether it's generative AI , Agentic AI , but creating a platform that's our customer .

Hock Tan: Be it for enterprise consumption in code assistance of agentic AI, or be it for consumer subscription that we know about. Whatever it is that few prospects, and many of whom are customers now, who are creating this, Whether it's generative AI, agentic AI, but creating a platform. That's our customer. With respect to each of those guys, we are seeing very stronger and stronger demand for compute capacity.

Speaker #1: And with respect to each of those guys , we are seeing very stronger and stronger demand for compute capacity

Speaker #5: For

Hock Tan: For training, which is something they do need constantly, but what is very, very interesting and surprising to us is very much for inference in order to productize the LLMs, their latest LLMs they create and monetize it. That inference is driving a substantial amount of compute capacity, which is great for us because these, all these players, these five, six customers of ours, are on the path to creating their own custom accelerators. Beyond that, their own design architecture of networking clusters of those custom accelerators. I think we're going to see demand keeps picking up as we've heard announcements in the past six months.

Speaker #1: For training , which is something they do need constantly . But what is very , very interesting and surprising to to us is very much .

Speaker #1: For inference in order to productize the LMS , their latest LMS , they create and monetize it . And that inference is driving a substantial amount of compute capacity , which is great for us because this or these players , these five , or six customers of ours are on the path to creating their own custom accelerators .

Hock Tan: Beyond that, their own design architecture of networking clusters of those custom accelerators. I think we're going to see demand keeps picking up as we've heard announcements in the past six months.

Speaker #1: And beyond that , they are they own design architecture of a networking clusters of those customers So I think we're going to see demand keeps picking up as we've heard announcements in the past six months .

Hock Tan: Now, to clarify your first part, Blayne, when I say we forecast, we have a line of sight that our revenue in 2027 will be significantly in excess of $100 billion, I'm focusing on the fact that these are pretty much all based on chips. Whether they are XPUs, whether they are switch chips, DSPs, these are silicon content we're talking about.

Speaker #1: Now to clarify , your first part , Blayne , when I say we forecast . We have a line of sight that our revenue in 27 will be significantly in excess of 100 billion .

Speaker #1: I'm focusing on the fact that these are pretty much all based on chips , whether they are exposed , whether they are switched chips , DSP , these are silicon content .

Speaker #1: We're talking about

Blayne Curtis: Thanks so much.

Speaker #4: Thanks so much .

Operator: One moment for our next question. That will come from the line of Harlan Sur with J.P. Morgan. Your line is open.

Speaker #3: One moment for our next question . And that will come from the line of Harlan Sur with JP Morgan . Your line is open .

Harlan Sur: Yeah, good afternoon. Thank you for taking my question, and congratulations to the team on the strong results. Hock, you know, there's been a lot of noise around CSPs and hyperscalers embarking on their own internal XPU, TPU design efforts, right? We call it COT or customer-owned tooling. This is not a new dynamic with ASICs, right? I think the Broadcom team has been through this COT competitive dynamic before over the 30 years, right, that you've been a leader in the ASIC industry. Very few of these COT initiatives have ever been successful. Now on AI, some of these COT initiatives are coming to the market now, but it looks like they're at least 2x less performant than your current generation solutions, 2x less complex in terms of chip design complexity, packaging complexity, IP. Maybe just a quick two-part question.

Speaker #6: Yeah . Good afternoon . Thank you for taking my question . And congratulations to the team on the strong results . There's been a lot of noise around CSPs and hyperscalers embarking on their own internal xpu TPU design efforts .

Speaker #6: Right ? We call it CLT or customer owned tooling This is not a new dynamic with Asics , right ? I think the Broadcom team has been through this competitive dynamic before over the 30 years , right , that you've been a leader in the ASIC industry and very few of these coat initiatives have ever been successful , not on AI .

Harlan Sur: Very few of these COT initiatives have ever been successful. Now on AI, some of these COT initiatives are coming to the market now, but it looks like they're at least 2x less performant than your current generation solutions, 2x less complex in terms of chip design complexity, packaging complexity, IP. Maybe just a quick two-part question.

Speaker #6: Some of these initiatives are coming to the market now, but it looks like there is at least 2x less performance in your current generation solutions to 2x less complexity in terms of chip design, complexity, packaging complexity, and IP.

Speaker #6: So maybe just a quick two-part question. Hawk, one for you is, given your visibility into next year, do you see these science projects taking any meaningful TPU, XPU share from Broadcom?

Harlan Sur: Hock, one for you is, given your visibility into next year, do you see these COT science projects taking any meaningful TPU, XPU share from Broadcom? Then maybe the second quick question for either you or Charlie is, given that Broadcom's TPU, XPU programs from a performance complexity IP perspective are 12 to 18 months ahead of any of these COT programs, how does the Broadcom team widen this gap further?

Speaker #6: And then maybe the second quick question for either you or Charlie is given that Broadcom's TPU Xpu programs from a performance complexity IP perspective are 12 to 18 months ahead of any of these programs .

Speaker #6: How does the Broadcom team why this gap further ?

Hock Tan: Well, that's a great question. You know, it fits into that I purposely took the time in my opening remarks to say that when any of our any, I guess, hyperscaler or LLM developer tries to create, become self-sufficient entirely in creating what you call a customer-owned tooling or COT model, they face tremendous challenges. One is technology, which is as a technology as it relates to creating the silicon chips, and particularly in XPUs, that they need to do the computing and that, and that they, that's needed to optimize and run the train the an inference on the workloads they produce or their LLM. That technology we talked about comes from, comes in from different dimensions. You need the best silicon design team around.

Speaker #1: Well , that's a great question . And you know , it's it fits into that . I purposely took the time in my in my opening remarks to to say that when any of our any I guess hyperscaler or LM developer tries to create become self-sufficient entirely in creating what you call a customer owned tooling or cost model , they face tremendous challenges .

Hock Tan: One is technology, which is as a technology as it relates to creating the silicon chips, and particularly in XPUs, that they need to do the computing and that, and that they, that's needed to optimize and run the train the an inference on the workloads they produce or their LLM. That technology we talked about comes from, comes in from different dimensions. You need the best silicon design team around.

Speaker #1: One is technology , which is as technology as it relates to creating the silicon chips in , particularly in Expas , that they need to do the computing .

Speaker #1: And then, and then, they are what's needed to optimize and run the training and inference on the workloads they produce, or their LLM.

Speaker #1: It's , it's it's that technology we talked about comes from comes in from different dimensions . You need the best silicon design team around .

Hock Tan: You need cutting edge, really cutting edge SerDes, very advanced packaging. Just as much, you need to understand how to network clusters of them together. We've been doing this for 20 years, more than 20 years in silicon. In this particular space today, in generative AI, if you're trying to, as an LLM player, to do your own chip, you cannot afford to have a chip that is just good enough. You need the best chips that is around because you're competing against other LLM players. Most of all, you're also competing against Nvidia, who is by no means letting down their guard. They are producing better and better chips with every passing generation.

Speaker #1: You need cutting edge , really cutting edge surges , very advanced packaging and and most and just as much you need to understand how to network clusters of them together .

Speaker #1: We've been doing this for 20 years . More than 20 years . In silicon and in in in this particular space today , in generative AI , if you're trying to as an LM player , to do your own chip , you cannot afford to have a chip that is just good enough .

Hock Tan: You need the best chips that is around because you're competing against other LLM players. Most of all, you're also competing against Nvidia, who is by no means letting down their guard. They are producing better and better chips with every passing generation.

Speaker #1: You need the best chips there is around because you're competing against other players . And most of all , you also competing against Nvidia , who is by no means letting down their guard .

Speaker #1: They are producing better and better chips with every passing generation . So you have to , as an LM trying to establish your platform in the world , have to create chips that are better than , if not competitive with , not just invidia , but all the other platform players that you're competing against .

Hock Tan: You have to, as an LLM trying to establish your platform in the world, have to create chips that are better than if not competitive with not just Nvidia, but all the other LLM platform players that you're competing against. For that, you really need, our belief, and we see that firsthand, a partner in silicon with the best technology, IP, and execution around. Very modestly, I would say we are by far way out there, and we will not see competition in COT for many years to come. It will come eventually, but we're still a long way off because the race which we see continues.

Speaker #1: And for that , you really need our belief . And we see that firsthand . The the partner in silicon with the best technology , IP and execution around and very modestly , I would say we are by far way out there .

Speaker #1: And we will not see competition in cost for many years to come . It will come eventually , but we're still a long way off because the race with which we see continues and one thing I ed in there , that is particularly unique to us when you create those silicon , you really have to get it up and running in high volume in production very quickly .

Hock Tan: One thing I add in there that is particularly unique to us, when you create a silicon, you really have to get it up and running in high volume in production very quickly, time to market. We are very, very experienced in doing that. Anybody can design a chip in a lab that works well. Can you produce 100,000 of those chips quickly at yields that you can afford? We don't see too many players in the world that can do that. Charlie?

Speaker #1: Time to market. We are very, very experienced in doing that. Anybody can design a chip in a lab that works well.

Speaker #1: Can you produce 100,000 of those chips quickly at yields that you can , that you can afford . And we don't see too many players in the world that can do that .

Charlie Kawwas: I think you covered it very well, Hock.

Speaker #1: Charlie ,

Speaker #7: I think you covered it very well

Ben Reitzes: Thank you, Hock. Thank you, Charlie.

Speaker #6: Thank you Hawk . Thank you Charlie .

Operator: One moment for our next question. That will come from the line of Ross Seymore with Deutsche Bank. Your line is open.

Speaker #3: One moment for our next question. And that will come from the line of Ross Seymore with Deutsche Bank. Your line is open.

Ross Seymore: Hi. Thanks for letting me ask a question. Hock, in your script, you leaned a little bit more into the networking differentiation than you have in the past. I guess, kind of a short-term and a longer-term question. The short-term is, what's driving that up to 40% of the AI revenues? The longer-term question is, that percentage mix in that $100 billion plus, is that changing now? What sort of leadership do you expect to maintain in that business, whether it's scale out or scale up? Is your leadership position there helping on your XPU side as you can optimize across both the compute and the networking sides?

Speaker #8: Hi . Thanks for having me ask a question . How can your script . You leaned a little bit more into the networking differentiation than you have in the past .

Speaker #8: So I guess kind of a short term and a longer term question . The short term is what's driving that up to 40% of the AI revenues .

Speaker #8: And then longer term question is , is that going that percentage mix in that 100 billion plus , is that changing now ? What sort of leadership do you expect to maintain in that business , whether it's scale out or scale up ?

Speaker #8: And is your leadership position there helping on your XPU side, as you can optimize across both the compute and the networking sides?

Hock Tan: Well, let's address the first part of that fairly complex question first, Ross. Yes, in networking, especially, you know, with the new generation of GPUs, XPUs that are coming out there, we're running at 200 Gb SerDes out there in terms of bandwidth. The Tomahawk 6 that we introduced over 6 months ago, in fact, closer to 9 months ago, we're the only one out there. Our customers and the hyperscalers wants to run with the best networking and with the most bandwidth out there for their clusters. We are seeing huge demand for this only some 100 Tb per second switch out there. That's driving a lot of the demand. Couple that with running bandwidth on scaling out optical transceivers at 1.6 Tb.

Speaker #1: Well , let's address the first part of that fairly complex question . First , Ross . Yes , in networking , especially the , you know , with the new generation of GPUs , exposures that are coming out there where we're running at 200 gigabit , gigabit service out there in terms of bandwidth and the Tomahawk six that we introduced over six months ago .

Speaker #1: In fact , over closer to nine months ago , we're the only one out there . And our customers and the hyperscalers wants to run with the best networking and with the most bandwidth out there for their clusters .

Hock Tan: Our customers and the hyperscalers wants to run with the best networking and with the most bandwidth out there for their clusters. We are seeing huge demand for this only some 100 Tb per second switch out there. That's driving a lot of the demand. Couple that with running bandwidth on scaling out optical transceivers at 1.6 Tb.

Speaker #1: So we are seeing huge demand for this only 100 terabits per second switch out there . So that's driving a lot of demand .

Speaker #1: And couple that with running bandwidth on scaling out optical transceivers at 1.6TB , where again , the only player out there doing DSP at 1.6TB , that combination is driving , I would say the growth of our networking components even faster than our exports are growing , which is already pretty remarkable .

Hock Tan: We are, again, the only player out there doing DSP at 1.6 Tb per second. That combination is driving, I would say, the growth of our networking components even faster than our XPUs are growing, which is already pretty remarkable. That's what you're seeing. At some point, I would think these things will settle down, though we're not slowing down the pace because as I said, next year in 2027, we'll launch next generation Tomahawk 7, 2 extra performance, and we'll probably be the, by far, the first out there, and then we'll continue to sustain that momentum. At the end of the day, to answer your question, yeah, I expect as a composition of our total AI revenue in any quarter that we'll be ranging between probably 33% to 40% AI networking components.

Speaker #1: So that's what you're saying . But at some point , I would think these things will settle down , though we're not slowing down the pace because as I said , next year in 27 will launch next generation Tomahawk seven two .

Hock Tan: At some point, I would think these things will settle down, though we're not slowing down the pace because as I said, next year in 2027, we'll launch next generation Tomahawk 7, 2 extra performance, and we'll probably be the, by far, the first out there, and then we'll continue to sustain that momentum. At the end of the day, to answer your question, yeah, I expect as a composition of our total AI revenue in any quarter that we'll be ranging between probably 33% to 40% AI networking components.

Speaker #1: The performance and will probably be the by far the first out there . And that will continue to drive sustain that momentum . And but at the end of the day , to answer your question , yeah , I expect as a composition of our total AI revenue and any quarter that will be ranging between probably 33% to 40% , AI networking components

Ross Seymore: Great. Thanks, Hock.

Hock Tan: Thanks.

Speaker #8: Great . Thanks . Hawk . Thanks .

Operator: One moment for our next question. That will come from the line of C.J. Muse with Cantor Fitzgerald. Your line is open.

Speaker #3: One moment for our next question. And that will come from the line of CJ Muse with Canaccord. Fitzgerald, your line is open.

C.J. Muse: Yeah. Good afternoon. Thank you for taking the question. I'm curious, you know, how are you thinking about the move to disaggregate prefill and decode from the GPU ecosystem and the impact to custom silicon demand? Are you seeing any potential changes in sort of the relative mix between GPUs and custom silicon?

CJ Muse: Yeah. Good afternoon. Thank you for taking the question. I'm curious, you know, how are you thinking about the move to disaggregate prefill and decode from the GPU ecosystem and the impact to custom silicon demand? Are you seeing any potential changes in sort of the relative mix between GPUs and custom silicon?

Speaker #9: Yeah . Good afternoon . Thank you for taking the question . I'm curious . You know , how are you thinking about the move to disaggregate prefill and decode from the GPU ecosystem and the impact of custom silicon demand ?

Speaker #9: Are you seeing any potential changes in, sort of, the relative mix between GPUs and customers? Silicon.

Hock Tan: I'm not sure I fully understand your question, CJ. CJ, could you clarify what you mean disaggregate?

Speaker #1: I'm not sure I fully understand your question, CJ. Could you clarify what you mean? Disaggregate?

C.J. Muse: Sure. You know, pushing off workloads to CPX for pre-fill and working off of Groq for decode, and, you know, having that disaggregated kind of world. Does that put, you know, any pressure in terms of the demand for custom versus going with, you know, a full GPU stack?

CJ Muse: Sure. You know, pushing off workloads to CPX for pre-fill and working off of Groq for decode, and, you know, having that disaggregated kind of world. Does that put, you know, any pressure in terms of the demand for custom versus going with, you know, a full GPU stack?

Speaker #9: Sure . You know , pushing off workloads to to CP for Prefill and working off of graph for for decode and you know , having that disaggregated kind of world and does that put any pressure in terms of the demand for custom versus going with , you know , a full GPU stack ?

Hock Tan: Okay. I get what you mean. That word disaggregation kind of threw me off. In a way, what you're really saying is how is the architecture of AI accelerator, be it GPU or XPU evolving as workloads starts to evolve? That's what we are seeing very much in particular. The one-size-fits-all with general purpose GPU gets you only that far. It can still keep going on because you can still run different workloads. Like you run mixture of experts, even though you want to run mixture of experts with sparse cores to be very effective, you hear the term. In a GPU, you're designed for dense matrix multiplication.

Speaker #1: Okay , I get I get what you mean that that would kind of took me off . What you in a way what you're really saying is what how is the architecture of AI accelerator , be it GPU or Xpu evolving as workloads starts to evolve ?

Speaker #1: And that's what we are seeing very much . In particular , the one size fits all of a general purpose GPU gets you only that far .

Speaker #1: You can still keep going on because you can still run different workloads like you run mixture of experts , even though you have .

Speaker #1: You want to run . Make experts with sparse cores to be very effective . You hear the term , but in a GPU you're designed for dense matrix multiplication , so you do it with software kernels .

Hock Tan: You do it with software kernels, but it's not as effective as you'd hard code it in silicon and make those XPUs purposely designed to be much more performing for mixture of expert workloads, say. The same applies for inference. What that drives down to is you start to see designs of XPUs become much more customized for particular workloads of particular LLM customers of ours. The design starts to depart from what is the traditional standard GPU design. Which is why, as we always indicated before, XPUs will eventually be more the choice simply because it will allow flexibility in making designs that work with particular workloads, one for training even, and one for inference. As you say, one perhaps will be better at pre-filling and one to be better at post-training or reinforced learning or test time scaling.

Speaker #1: But it's not as effective as you hard code it in silicon . And make those experts purposely designed to be much more performing for mixture of expert workloads , say the same applies for inference .

Speaker #1: And what what that drives down to is you start to see designs of experts become much more customized for particular workloads . Particular LM customers of ours and the and the design starts to depart from what is the traditional standard GPU design , which is why , as we always indicated before , experts will effectively will eventually be more the choice simply because it will allow flexibility in making designs that work with particular workloads .

Hock Tan: Which is why, as we always indicated before, XPUs will eventually be more the choice simply because it will allow flexibility in making designs that work with particular workloads, one for training even, and one for inference. As you say, one perhaps will be better at pre-filling and one to be better at post-training or reinforced learning or test time scaling.

Speaker #1: One for training and one for inference . And as you say , one perhaps would be better at prefilling and want to be better at Post-training or reinforce learning or test time scaling .

Hock Tan: You can tweak your TPUs towards the XPU, sorry, Freudian slip, to a particular kind of workload LLM that you want. We're seeing that. We're seeing that roadmap in all our five customers.

Speaker #1: You can tweak your TPUs towards the expert . Sorry , Freudian slip to a particular kind of workload . LM that you want , and we think that we think that roadmap in all our five customers

Operator: One moment for our next question. That will come from the line of Timothy Arcuri with UBS. Your line is open.

Speaker #3: One moment for our next question. And that will come from the line of Timothy Arcuri with UBS. Your line is open.

Timothy Arcuri: Thanks a lot. I had just a question on sort of the puts and takes on gross margin as you begin to ship these racks. I mean, obviously it's gonna pull the blended margin down, but I'm wondering if there's any guardrails you can give us on this. It seems like the racks are maybe 45%, 50% gross margin. I guess should we think about that pulling gross margin down like 500 basis points roughly as these racks begin to ship? I guess, you know, part of that, Hock, is there some, like, floor to the gross margin, you know, below which you wouldn't be willing to do, you know, more racks? Thanks.

Speaker #10: Thanks a lot . I had just a question on sort of the puts and takes on gross margin as you begin to ship these racks .

Speaker #10: I mean, obviously it's going to pull the blended margin down, but I'm wondering if there's any guardrails you can give us on this.

Speaker #10: It seems like the racks are maybe 45 to 50% gross margin. So, I guess, should we think about that pulling gross margin down, like 500 basis points roughly, as these racks begin to ship?

Speaker #10: And I guess part of that hawk is there some floor to the gross margin . You know , below which you wouldn't be willing to do ?

Speaker #10: You know , more racks ? Thanks

Hock Tan: I hate to tell you that. You must be a bit hallucinating. Our gross margin is solidly at the number Kirsten reports. We will not be affected by the gross margin and by more and more AI products going out. We have gotten our yields, we've gotten our cost to the point where the model we have in AI will be fairly consistent with the models we have in the rest of the semiconductor business. Kirsten?

Speaker #1: To tell you that you must be a bit hallucinating . Our gross margin is solidly at the number . Kirsten reports . We will not be affected by the gross margin and by more and more AI products going out .

Speaker #1: We've gotten our yields regarding our cost to the point where the model we have in AI will be fairly consistent with the models we have in the rest of the semiconductor business .

Kirsten Spears: I would agree with that. I think on further study, relative to even comments that I did make last quarter, the impact relative to our overall mix is actually not gonna be substantial at all. I wouldn't worry about it.

Speaker #1: Yes .

Speaker #2: I would agree with that . I think on further study relative to even comments that I did make last quarter , the impact relative to our overall mix is actually not going to be substantial at all .

Speaker #2: I wouldn't worry about it .

Timothy Arcuri: Oh, okay. Thank you so much.

Speaker #10: Okay. Thank you so much.

Operator: One moment for our next question.

Speaker #3: One moment for our next question . And that will come from the line of Stacy Rasgon with Bernstein . Your line is open .

Timothy Arcuri: Oh.

Operator: That will come from the line of Stacy Rasgon with Bernstein. Your line is open.

Stacy Rasgon: Hi, guys. Thanks for taking my question. I don't know if this is for Hock or Kirsten, but I wanted to dig in a little more to this substantially more than $100 billion next year. I'm trying to just count up the gigawatts. I counted, I don't know, 8 or 9. You have 3 from Anthropic, 1 from OpenAI, so that's 4. You said Meta was multiple, so at least 2. That gets you to 6. Google, I figure, should be bigger than Meta, so like at least 3. You know, that's 9, and then you got a few others. I had thought that your content per gigawatt was sort of, you know, call it in the $20 billion per gigawatt range. I guess what I'm asking is my math around the gigawatts you plan to ship in 2027 correct?

Speaker #11: Hi , guys . Thanks for taking my question . I don't know if this is for a hacker person , but I wanted to dig in a little more to this .

Speaker #11: Substantially more than 100 billion next year . I'm trying to just count up the gig of gigawatts . I counted , I don't know , 8 or 9 .

Speaker #11: You have three from Anthropic, one from OpenAI. So that's four for you. You said Meta was multiple, so at least two.

Stacy Rasgon: Google, I figure, should be bigger than Meta, so like at least 3. You know, that's 9, and then you got a few others. I had thought that your content per gigawatt was sort of, you know, call it in the $20 billion per gigawatt range. I guess what I'm asking is my math around the gigawatts you plan to ship in 2027 correct?

Speaker #11: That gets you to six . Google I figure should be bigger than meta . So like at least three , you know that's nine .

Speaker #11: And then you got a few others . I thought that your content per gigawatt was sort of , you know , call it the $20 billion per gigawatt range .

Speaker #11: I guess what I'm asking is my math around the gigawatts you plan to ship in 27 , correct . And how do I think about your content per gigawatt ?

Stacy Rasgon: How do I think about your content per gigawatt as that ships? Maybe it will be quote-unquote, "Substantially more than $100 billion.

Speaker #11: Is that chips? Maybe it will be, quote unquote, substantially more than $100 billion.

Hock Tan: Stacy, you have a very interesting perspective, and I got to admire you for that. You're right. You can look at it at gigawatts, which is the right way to look at it instead of dollars, 'cause that's how we sell our chips. You have to realize, depending on our LLM customer, our 6 customers now. Sorry, not 5, 6. The dollars per gigawatt chip dollars varies, sometimes quite dramatically. It does vary, you're right. It's not far from the dollars you're talking about. If you look at it by gigawatt in 2027, we are seeing it getting close to 10 gigawatts.

Speaker #1: But you see , you have a very interesting perspective . And I got a mind you for that . But you're right . You can at gigawatts , which is the right way to look at it instead of dollars , because that's how we sell our chips to .

Speaker #1: You have to realize we it depending on our LM customer , our six customers . Now sorry , not 566 . The dollars per gigawatt chip dollars varies , sometimes quite dramatically .

Speaker #1: It does vary , but you're right , it's not far from the dollars you're talking about . And if you look at it by gigawatt in 27 .

Speaker #1: We are saying again, close to ten gigawatts.

Stacy Rasgon: Got it. That's very helpful. Thank you.

Speaker #11: Got it. That's very helpful. Thank you.

Hock Tan: Sure.

Operator: Our next question that will come from the line of Ben Reitzes with Melius Research. Your line is open.

Speaker #3: And our next question that will come from the line of Ben Reitzes with Melius Research . Your line is open .

Ben Reitzes: Hey, thanks, Hock, great to be speaking with you. Wanted to ask you about your commentary about supply visibility on those four major components through 2028. You know, A, how'd you do it? This is probably the you know, you're the first one to kind of go out through the 2028 timeframe. Secondly, after this astounding growth in 2027 for your AI business, do you have enough visibility to grow quite a bit in 2028, based on the supply that you see and that kind of commentary? Thanks a lot.

Speaker #12: Hey, thanks. I'm Hock. Great to be speaking with you. I wanted to ask you about your commentary about supply visibility on those four major components through 2028.

Speaker #12: You know , a how'd you do it ? This is probably the the you know , you're the first one to kind of go out through the 28 time frame and secondly , after this astounding growth in 2027 for your AI business , do you have enough visibility to grow quite a bit in 2028 based on the supply that you see and that kind of commentary Thanks a lot .

Hock Tan: The best answer is, yeah, you're right. We anticipate this sharp accelerated growth. Now, nobody could anticipate the rate of growth it's showing, but we kind of anticipate a large part of it, or I guess, or for the longer than six months. We were early in being able to lock up T-glass, the infamous T-glass you all heard about. We were very early. We've locked up substrates. We have worked on our good partners on the rest of the stuff we talked about. The answer to your question is, it's somewhat anticipation early and the fact that we have very good partners out there in these key components. What else can I say except that, yes. Charlie, you want to add anything?

Speaker #1: The best answer is , yeah , you're right . We we anticipate this sharp , accelerated growth . Now , nobody could anticipate the rate of growth is showing .

Speaker #1: But we kind of anticipate a large part of it or I guess over the long or longer than six months , we were early in being able to lock up key glass for infamous T-class .

Hock Tan: We were early in being able to lock up T-glass, the infamous T-glass you all heard about. We were very early. We've locked up substrates. We have worked on our good partners on the rest of the stuff we talked about. The answer to your question is, it's somewhat anticipation early and the fact that we have very good partners out there in these key components. What else can I say except that, yes. Charlie, you want to add anything?

Speaker #1: You all heard about it. We were very early. We've locked up substrates. We have worked on our good partners on the rest of the stuff we talked about.

Speaker #1: And so the answer to your question is it's somewhat anticipation early . And the fact that we have very good partners out there in these key components can I say except that , yes , Charlie , you want to add anything ?

Charlie Kawwas: Yeah, just, maybe a couple of quick ones. I think you covered that piece really well. I think, Ben, the other piece that's really important, as Hock said, we build custom silicon for 6 customers. We have very deep strategic multi-year engagement with them. They share with us, because of this custom capability, exactly what they anticipate, at least over the next 2 to 3 years, sometimes 4 years. Because of that's exactly why we went and secured all the elements Hock talked about. When we secure this, it requires investments with these partners, sometimes developing not just more capacity, but the right technology and capacity for that. We have to go secure it for multiple years. We're probably, you're right. We're probably the first one to secure that up to 28 or beyond.

Speaker #7: Yeah . Just of quick ones . I think you covered that piece really well . I think Ben , the other piece that's really important .

Speaker #7: As hock , we build custom silicon for six customers . We have very deep , strategic , multi-year engagement with them . They share with us because of this custom capability , exactly what they anticipate , at least over the next 2 to 3 years , sometimes four years .

Speaker #7: And so because of that , that's exactly why we went and secured all the elements . Hock talked about . And when we secure this , it requires investments with these partners .

Charlie Kawwas: When we secure this, it requires investments with these partners, sometimes developing not just more capacity, but the right technology and capacity for that. We have to go secure it for multiple years. We're probably, you're right. We're probably the first one to secure that up to 28 or beyond.

Speaker #7: Sometimes developing not just more capacity , but the right technology . And capacity for that . So we have to go secure it from years and we're probably you're right , we're probably the first one to secure that up to 28 or beyond .

Ben Reitzes: Can you grow in 2028 with what you see in supply? Sorry to sneak that in.

Speaker #12: And can you grow in 28 with what you see in supply . Sorry to sneak that in .

Hock Tan: Yes.

Speaker #13: Yes

Ben Reitzes: Thank you.

Speaker #12: Thank you

Operator: Thank you. Our next question that will come from the line of Vivek Arya with Bank of America Securities. Your line is open.

Speaker #3: Thank you. Our next question will come from the line of Vivek Arya with Bank of America Securities. Your line is open.

Vivek Arya: Thanks for taking my question. Hock, I just wanted to first clarify the Anthropic project you're doing, the $20 billion or so for a gigawatt this year, how much of that is chips, and how much of that is kind of racks? I just wanted to understand when you say $100 billion in chips, is it a distinction between chips versus your rack scale projects? Just that project is supposed to triple next year. My question is, you know, your AI business is transitioning from kind of one large customer that was, you know, where you had kind of exclusive partnership, to now multiple customers who are using multiple suppliers. How do you get the visibility and the confidence about, you know, how your share will progress at these multiple customers?

Speaker #14: Thanks for taking my question . How can I just wanted to first clarify the anthropic project you're doing the 20 billion or so for gigawatt this year .

Speaker #14: How much of that is chips, and how much of that is kind of racks? I just wanted to understand, when you say $100 billion in chips, is there a distinction between chips versus your rack-scale projects?

Vivek Arya: Just that project is supposed to triple next year. My question is, you know, your AI business is transitioning from kind of one large customer that was, you know, where you had kind of exclusive partnership, to now multiple customers who are using multiple suppliers. How do you get the visibility and the confidence about, you know, how your share will progress at these multiple customers?

Speaker #14: Because just that project is supposed to triple next year , and then my question is , you know , your AI business is transitioning from kind of one large customer that was , you know , where you had kind of exclusive partnership to now multiple customers who are using multiple suppliers .

Speaker #14: So how do you get the visibility and the confidence about how your share will progress at these multiple customers? Because it's, you know, it's a very kind of fragmented engagement that they have across a whole range of cloud service providers.

Vivek Arya: Because it's, you know, it's a very kind of fragmented engagement that they have across a whole range of cloud service providers. What are you doing to ensure that, you know, you have solid visibility and, you know, the right market share at this fragmented set of customers who are using multiple suppliers?

Speaker #14: And so what are you doing to ensure that , you know , you have solid visibility and you know the right market share at this fragmented set of customers who are using multiple suppliers ?

Hock Tan: Vivek, you have to understand one thing about. First, as Charlie correctly put down very nicely. We only have very few customers, to be precise, six. For the volume we're driving, the revenue we're driving, we only have just six. Prior to that, even less recently. Number two, also have to understand with the dollars each of them spend and the criticality of the nature of what they're embarking on, and that's why I threw out this term. Meta has MTIA. That's their AI, their custom accelerator program. To them, as to every one of my customers in this space, it's a strategic play. It's not optionality. To them, long term, short term, medium term is strategic, extremely strategic.

Speaker #1: Vivek , you have to understand one thing about first , as Charlie correctly put out very nicely , we only have very few customers to be precise .

Speaker #1: Six for the volume we are driving the the the revenue we're driving . We only have just six prior to that , even less recently .

Speaker #1: And number two, also, they have to understand the dollars each of them spend and the criticality of the nature of what they're embarking on.

Speaker #1: And that's why I throw out this term matter has media that's there . Are there are custom accelerator accelerator program to them as to every one of my customers in this space , it's a strategic play .

Hock Tan: That's their AI, their custom accelerator program. To them, as to every one of my customers in this space, it's a strategic play. It's not optionality. To them, long term, short term, medium term is strategic, extremely strategic.

Speaker #1: It's not optionality to them long term , short term , medium term is strategic , extremely strategic . They don't stop and they are very clear .

Hock Tan: They don't stop, they are very clear, each of them on where they want to position these custom silicon within their trajectory of their LLM development and the trajectory of how they develop inference for productizing those LLM. That part, we have very clear visibility. Anything else on GPU using Neo-Cloud, using cloud business, these are all transactional and optionality. You point out very correctly. It seems very confusing. Trust me, not for us, nor those customers we have. They're very strategic, they're very targeted, they know exactly what they're building up and how much capacity they want to build up each year. The only thing they think about is, can they do it faster? Otherwise, it's very strategic and targeted on a projected roadmap.

Speaker #1: Each of them on where they want to position these custom silicon within their the the trajectory of their LM development and the trajectory of how they develop inference for productizing those elements , that part we have very clear visibility .

Speaker #1: Anything else on GPU using new cloud hyper using cloud business . These are all transactional and optionality . So you have to split your you point out very correctly , it seems very confusing .

Hock Tan: Trust me, not for us, nor those customers we have. They're very strategic, they're very targeted, they know exactly what they're building up and how much capacity they want to build up each year. The only thing they think about is, can they do it faster? Otherwise, it's very strategic and targeted on a projected roadmap.

Speaker #1: Trust me. Not for us, nor those customers we have. They're very strategic, they're very targeted, and they know exactly what they're building up and how much capacity they want to build up each year.

Speaker #1: And the only thing they think about is, can they do it faster? Otherwise, it's very strategic and targeted on a projected roadmap.

Hock Tan: Anything else you see in the mix is pure, I call it, opportunistic for these guys, the optionality. It's very clear.

Speaker #1: Anything else you see in the mix is pure. I call it opportunistic for these guys. The optionality. So it's very clear.

Vivek Arya: On the clarification, Hock, Anthropic racks versus chips. Thank you.

Speaker #14: On the clarification, Hawk, Anthropic racks versus chips. Thank you.

Hock Tan: I'd rather not answer that, but we're okay. As Kirsten said, we're good on our dollars and margin.

Speaker #1: I'd rather not answer that. But we're okay. As Kirsten said, we're good on our dollars, and Margie.

Vivek Arya: Thank you. Thank you.

Speaker #15: Thank you .

Speaker #14: Thank you

Operator: Thank you. Our next question will come from the line of Thomas O'Malley with Barclays. Your line is open.

Speaker #3: , thank you . Our next question that will come from the line of Tom O'Malley with Barclays . Your line is open .

Thomas O'Malley: Hey, guys. Thanks for taking my questions. I have one for Hock and one for Charlie. Hock, I know you're very specific, in particular, about what you put in the preamble, and you noted that customers are staying at direct attached copper through 400 gig SerDes. Is there any reason you're pointing that out in particular, especially as a leading pioneer in CPO? On Charlie's side, as you're adding more customers here, I would imagine customers that design ASICs with you are gonna use scale-up Ethernet. Maybe talk about scale-up protocols and how you see Ethernet developing there as well. Thank you.

Speaker #11: Hey , guys . Thanks for taking my questions . I have one for Hawke and one for Charlie . So , Hawke , I know you're very specific in particular about what you put in the preamble , and you noted that customers are saying a direct attached copper through 400 gig .

Speaker #11: Is there any reason you're pointing that out in particular , especially as a leading pioneer in CPO ? And then on Charlie's side , as you're adding more customers here , I would imagine customers with designs are going to use scalable Ethernet , maybe talk about scale up protocols and how you see Ethernet developing there as well

Hock Tan: Okay. No, unless. I'm just highlighting the fact that we're On networking, our technology is really very, very uniquely positioning us to help our customers. More than our customers, even customers using general purpose GPUs, not just XPUs. Which is that, you know, if you are trying to create LLMs and creating your own AI data centers and designing it, architecting it, you truly want larger and larger domains or clusters for... You really want to connect XPUs to XPUs directly where you can. The best way to do that is use direct attached copper. That's the lowest latency, lowest power, and lowest cost. You want to keep doing that, especially in scale up, as long as possible. In scaling out, we're past that. We use optical. That's fine.

Speaker #1: Okay , no . I'm just highlighting the fact that we're on networking our technology is really very , very uniquely positioning us to help our customers .

Speaker #1: And more than our customers , even customers using general purpose GPUs , not just express , which is that , you know , if you are running a trying to create LMS and running , creating your own AI data centers and designing it , architecting it , you truly want larger and larger domains or clusters for .

Speaker #1: And you really want to connect experts to experts directly where you can. And the best way to do that is use direct attach copper.

Hock Tan: That's the lowest latency, lowest power, and lowest cost. You want to keep doing that, especially in scale up, as long as possible. In scaling out, we're past that. We use optical. That's fine.

Speaker #1: That's the lowest latency , lowest power , and lowest cost . So you want to keep doing that , especially in scale up .

Speaker #1: As long as possible . In scaling out . We're past that . We use optical . That's fine . But I'm talking about scaling up in a rank in a cluster domain .

Hock Tan: I'm talking about scaling up in a rack, in a cluster domain. You really want to use direct attached copper as long as you can. We are still, based on our technology that Broadcom has, especially on connecting XPU to XPU or even GPU to GPU, we can do it with copper, and we can push the envelope from 100G to 200G to even to 400G. We have SerDes now running 400G that can drive distance on a rank to run copper. All I'm trying to say is, you don't need to go run into some bright, shiny objects called CPO, even as we are the lead in CPOs. CPOs will come in its time. Not this year, maybe not next year, but in its time. Charlie?

Speaker #1: You really want to use direct attach copper as long as you can. And we are still based on our technology that Broadcom has with ON, especially on connecting CPU to XPU or even GPU to GPU.

Speaker #1: We can do it with copper and we can push the envelope from 100 gig G to 200 G to even to 400 G .

Speaker #1: We have 30 now running 400 G that can drive distance on a ring to run copper . What ? All I'm trying to say is you don't need to go run into some bright , shiny objects called CPO .

Hock Tan: All I'm trying to say is, you don't need to go run into some bright, shiny objects called CPO, even as we are the lead in CPOs. CPOs will come in its time. Not this year, maybe not next year, but in its time. Charlie?

Speaker #1: Even as we are the lead in CPO . CPO will come in . Its time . Not this year . Maybe not next year , but in its time .

Charlie Kawwas: Yeah. No. Well, well said, Hock. On the question of Ethernet, with the debut of the cloud, Ethernet became the de facto standard in every cloud for the last two decades. If you look at the debut of the backend networks, as Hock articulated, there was two years ago a big fight about what protocol should be used to achieve the latency, the scale necessary on scale-out. The industry at the time, 24 months ago, was not clear. We were clear. We were very clear actually about what the answer should be. Again, because of the deep engagements with our partners, they made it very clear to all of us and the industry, GPU or XPU, that Ethernet is the scale-out of choice. Check mark. Today, everyone is talking about scaling out with Ethernet.

Speaker #1: Charlie .

Speaker #13: Yeah. No. Well... Well.

Speaker #7: Said. And on the question of Ethernet, with the debut of the cloud, Ethernet became the de facto standard in every cloud for the last two decades.

Speaker #7: If you look at the debut of the back end networks as Hawk articulated, there was, two years ago, a big fight about what protocol should be used to achieve the latency, the scale necessary on scale out, and the industry at the time.

Speaker #7: 24 months ago was not clear . We were clear we were very clear , actually , about what the answer should be . And again , because of the deep engagements with our partners , they made it very clear to all of us and the industry , GPU or Xpu that Ethernet is the scale out of choice .

Charlie Kawwas: We were clear. We were very clear actually about what the answer should be. Again, because of the deep engagements with our partners, they made it very clear to all of us and the industry, GPU or XPU, that Ethernet is the scale-out of choice. Check mark. Today, everyone is talking about scaling out with Ethernet.

Speaker #7: Check Mark today everyone is talking about scaling out with Ethernet . Now , when it comes to scale up , yes , exactly .

Charlie Kawwas: Now, when it comes to scale up, yes, exactly like what happened 3, 4 years ago, on scale up now, what's the right answer for this? What we're hearing consistently and what we're seeing is the right answer, is Ethernet. As you know, last year we've announced with multiple hyperscalers and many of our peers in the semiconductor industry that Ethernet scale-up is the right choice. That's what we believe will happen. Time will tell. A lot of the XPU designs we're doing, we're being asked to scale up through Ethernet, and we're happy to enable that.

Speaker #7: Like what happened three four years ago on scale up . Now , what's the right answer for this and what we're hearing consistently and what we're seeing is the right answer is Ethernet .

Speaker #7: And as you know, last year we announced with multiple hyperscalers and many of our peers in the semiconductor industry that Ethernet scale-up is the right choice.

Speaker #7: That's what we believe will happen. Time will tell. But a lot of the XPU designs we're doing, we're being asked to scale up through Ethernet, and we're happy to enable that.

Thomas O'Malley: Thank you both.

Speaker #11: Thank you both .

Operator: Thank you. Our next question that will come from the line of James Schneider with Goldman Sachs. Your line is open.

Speaker #3: Thank you. And our next question will come from the line of Jim Schneider with Goldman Sachs. Your line is open.

James Schneider: Good afternoon, and thanks for taking my question. Hock, it was helpful to hear you discuss the progress of your other full custom XPU engagements outside of TPUs. As we look into next year, is it fair to assume that those are mostly targeting inference applications or not? Could you maybe qualitatively speak to either the performance or cost advantages relative to GPUs that is giving those customers the ability to a large scale? Thank you.

Speaker #16: Good afternoon. Thanks for taking my question, Hawk. It was helpful to hear you discuss the progress of your other full custom XPU engagements outside of TPUs.

Speaker #16: As we look into next year , is it fair to assume that those are mostly targeting inference applications or not ? And then could you maybe qualitatively speak to either the performance or cost advantages relative to GPUs that are giving those customers the ability to , to forecast in such a large scale ?

Hock Tan: Thanks. It's, you know, most of our customers begin with inference, simply because that tends to be, you know, that tends to be the easiest path to start on. Not necessarily from anything else than the fact that, you know, when you do inference, it's less compute. Also the question is, do you need this general purpose, massive dense matrix multiplication GPUs, when you can do it more efficiently, effectively with customs inference, silicon XPUs, that do the job better or just as well, much cheaper cost, lower power. That's what we find these customers starting with. They are now in training, and many of our XPUs are used both in training as well as inference.

Speaker #16: Thank you .

Speaker #1: Thanks . It's , you know , most of our customers begin with inference simply because that tends to be , you know , that tends to be the easiest path to start on , not necessarily from anything else .

Speaker #1: Then the fact that , you know , when you do inference , it's much it's less compute . But also then the question is , do you need this general purpose massive dense matrix multiplication ?

Hock Tan: Also the question is, do you need this general purpose, massive dense matrix multiplication GPUs, when you can do it more efficiently, effectively with customs inference, silicon XPUs, that do the job better or just as well, much cheaper cost, lower power. That's what we find these customers starting with. They are now in training, and many of our XPUs are used both in training as well as inference.

Speaker #1: GPUs, when you can do it more efficiently and effectively with custom inference or silicon, GPU use that does the job better or just as well, much cheaper, lower cost, and lower power.

Speaker #1: And that's what we find. These customers, starting with Q1. But they are now in training, and many of our experts are used both in training as well as inference.

Hock Tan: By the way, they are interchangeable, just a GPGPU can be used not just for training, which they are perhaps more perfectly suited to, but they can be used for inference. What we are seeing is our XPUs are used for both. We are seeing that going on. We are also seeing very rapidly more for those customers who are much more matured in the progression I talked about in their journey towards complete XPU, that they will start to develop two chips each year simultaneously, one for training, one for inference to be specialized. Why? Because what we are seeing very clearly for this LLM players is, you do the training to achieve a higher level of intelligence smarts for your LLM. Great, you get yourself a great LLM state-of-the-art or more. Now you've got to productize it, which means inference.

Speaker #1: And by the way , they are interchangeable . Just as GPU can be used not just for training , which they are perhaps more perfectly suited to , but they can be used for inference .

Speaker #1: What we think is our experts are used for both, and we're seeing that going on, but we're also seeing very rapidly more for those customers who are much more mature in the progression.

Hock Tan: We are also seeing very rapidly more for those customers who are much more matured in the progression I talked about in their journey towards complete XPU, that they will start to develop two chips each year simultaneously, one for training, one for inference to be specialized. Why? Because what we are seeing very clearly for this LLM players is, you do the training to achieve a higher level of intelligence smarts for your LLM. Great, you get yourself a great LLM state-of-the-art or more. Now you've got to productize it, which means inference.

Speaker #1: I talked about, in their journey towards complete expertise, that they will start to develop two chips each year simultaneously—one for training and one for inference—to be specialized.

Speaker #1: Why ? Because what we're seeing very clearly for these players , LM players is you do the training to get to achieve a higher level of intelligence smarts for your LM .

Speaker #1: So great . You get yourself a great LM , state of the art or more . Now you got the Productize . It , which means inference .

Hock Tan: Well, you can then decide at that time you got your model going as the best, because if you decide then to do your inference productization, it'll take you a year at least to productize. At which time, somebody else is gonna create an LLM better than yours. There's a leap of faith here that when you do training to create the next level of super intelligence in your LLM, you have to be investing simultaneously in inference, both in terms of the chip and the capacity. Our visibility is really coming out better and better as we find those six customers get more matured in their progression towards better and better LLMs. Yeah, that is the trend we are seeing.

Speaker #1: Well , you can't then decide at that time you got your your model going as the best . Because if you decide then to do your inference productization , you take you a year at least to productize , at which time somebody else is going to create an LM better than yours .

Hock Tan: There's a leap of faith here that when you do training to create the next level of super intelligence in your LLM, you have to be investing simultaneously in inference, both in terms of the chip and the capacity. Our visibility is really coming out better and better as we find those six customers get more matured in their progression towards better and better LLMs. Yeah, that is the trend we are seeing.

Speaker #1: So you, there's a leap of faith here that when you do training to create the next level of superintelligence in your LM, you have to be investing simultaneously in inference, both in terms of the chip and the capacity.

Speaker #1: So our visibility is really coming out better and better as we find those six customers get more mature in their progression towards a better and better LMS.

Speaker #1: So yeah, that is the trend we are seeing. It's not happening to all our six customers yet, but we are seeing a majority of them headed in that way right now.

Hock Tan: It's not happening to all our six customers yet, but we are seeing a majority of them headed in that way right now.

Operator: Thank you. One moment for our next question. That will come from the line of Joshua Buchalter with TD Cowen. Your line is open.

Speaker #3: Thank you. One moment for our next question. And that will come from the line of Joshua Buckhalter with TD Cowen. Your line is open.

Joshua Buchalter: Hey, guys. Thanks for taking my question, and congrats on the results. Appreciate all the details on the expectations for deployments at specific customers. I was hoping you could just maybe reflect on how visibility has changed over the last 1 to 2 quarters that gave you the confidence to give us more details. On a specific one, you mentioned greater than 1 gigawatt for OpenAI in 2027. With that deal being for 10 gigawatts through 2029, that implies a pretty sharp inflection, I guess, in 2028. Is that the right way to think about it, and was that sort of always the plan? Thank you.

Speaker #17: Hey guys. Thanks for taking my question, and congrats on the results. I appreciate all the details on the expectations for deployment at specific customers.

Speaker #17: I was hoping you could just maybe reflect on how visibility has changed over the last 1 to 2 quarters . That gave you the confidence to give us more details , and then on a specific one , you mentioned greater than a gigawatt for OpenAI in 2027 .

Speaker #17: With that deal being for 10 gigawatts through 2029, that implies a pretty sharp inflection. I guess in 2028. Is that the right way to think about it?

Speaker #17: And was that sort of always the plan? Thank you.

Hock Tan: Yes. Well, yeah. As you all seen, and you all know, in this generative AI race that we are in now, and I shouldn't use the word race, let's call it progression, among the few players we see here. I mean, it's a competition. Each is trying to create an LLM better than the other and more tailored for specific purpose, be they enterprise, be they consumer, be they search. Each one is trying to create it more and more. All of that requires not just training, which is important to keep improving your LLM models, but inference for productization and monetization of your LLMs. Probably call it the fact that we've been engaged with some of them now for more than a couple years.

Speaker #1: Yes. Well, yeah. This, as you all have seen and you all know, in this generative AI race that we are in now—and I shouldn't use the word 'race.'

Speaker #1: Let's call it progression among the few players we see here. I mean, it's a competition. Each is trying to create an LLM better than the other.

Speaker #1: And more tailored for specific purpose , be they enterprise , be they consumer , be they search , each one is trying to more and more and all of all of that requires not just training , which is important to keep improving your LLM models , but inference for Productization and monetization of your llms and we are getting and probably call it the fact that we've been engaged with some of them now for more than a couple of years .

Hock Tan: All of that requires not just training, which is important to keep improving your LLM models, but inference for productization and monetization of your LLMs. Probably call it the fact that we've been engaged with some of them now for more than a couple years.

Hock Tan: We're getting better and better visibility as they have more and more confidence that the XPUs they are working on with us is achieving what they're getting at. As they get a sense that the XPUs they are working on with the software, with the algorithm they needed, that they are having more confidence that this XPU silicon is what they need. It gets better and better. As it get better, we get more visibility, as Charlie puts up perfectly. At the end of the day, we only have six guys to work on. These six guys are all, as I said, they look at XPUs and AI in a very strategic manner. They don't think one generation at a time. They think multiple generation, multiple years.

Speaker #1: We're getting better in visibility as they have more and more confidence that the experts they are working on with us are achieving what they are getting at.

Speaker #1: As they get the sense that they experts , they are working on with the with the software , with the algorithms they needed that they are having more confidence that this exposed silicon is what they need and they want , and it gets better and better and it's get better .

Hock Tan: As it get better, we get more visibility, as Charlie puts up perfectly. At the end of the day, we only have six guys to work on. These six guys are all, as I said, they look at XPUs and AI in a very strategic manner. They don't think one generation at a time. They think multiple generation, multiple years.

Speaker #1: We get more visibility . As Charlie puts out perfectly , because at the end of the day , we only have six guys to work on , these six guys are all , as I said , look at exposed and AI in a very strategic manner .

Speaker #1: They don't think one generation at a time . They think multiple generations , multiple years . And in spite of all the hubris , noise out there on what's available , they are they think very long term on how they how they deploy the experts .

Hock Tan: In spite of all the hubris, noise out there on what's available, they think very long term on how they deploy the XPUs they develop with us. How they deploy in achieving better and better LLMs that they want to create, and more than that, how they deploy in monetizing. It's we are part of their strategic roadmap. We are not in just optionality of, Oh, shall I use a GPU? Shall I use in the cloud because I need to train for six months? No, this is more than that. The investment these guys are making are long term, and it's great to be part of that long-term roadmap as opposed to a transactional roadmap.

Speaker #1: They develop with us, how they deploy in achieving better and better LLMs than they want to create. And more than that, how they deploy in monetizing.

Speaker #1: So it's we are part of their strategic roadmap . We are not in just optionality of , oh , shall I use a GPU ?

Speaker #1: Shall I use it in the cloud? Because I need to train for six months? No, this is more than that.

Speaker #1: The investment these guys are making is long term, and it's great to be part of that long-term roadmap as opposed to a transactional roadmap.

Hock Tan: The noise, as I answered an early question, there's a lot of noise that mix up short-term transactions with what is long-term strategic positioning of our business and our product. To sum it all, I think our business in XPUs is a strategic, sustainable play for all the six customers we have today.

Speaker #1: And the noise, as I answered an earlier question from A, is there's a lot of noise that makes up short-term transactions with what is long-term strategic positioning of our business and our product.

Speaker #1: And to sum it all, I think our business in Exposed is a strategic, sustainable play for all the six customers we have today.

Operator: Thank you. Thank you. That is all the time we have for Q&A today. I would now like to turn the call back over to Ji Yoo for any closing remarks.

Speaker #17: Thank you .

Speaker #3: Thank you. That is all the time we have for Q&A today. I would now like to turn the call back over to Gu for any closing remarks.

Ji Yoo: Thank you, Sherie. Broadcom currently plans to report its earnings for Q2 of fiscal year 2026 after the close of market on Wednesday, 3 June 2026. A public webcast of Broadcom's earnings conference call will follow at 2:00 PM Pacific. That will conclude our earnings call today. Thank you all for joining. Sherie, you may end the call.

Speaker #18: Thank you. Sherry, Broadcom currently plans to report its earnings for the second quarter of fiscal year after the close of market on Wednesday, June 3, 2026.

Speaker #18: A public webcast of Broadcom's earnings conference call will follow at 2:00 p.m. Pacific. That will conclude our earnings call today. Thank you all for joining.

Speaker #18: Sherry, you may end the call.

Operator: This concludes today's program. Thank you all for participating. You may now disconnect.

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