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Marvell to Acquire Lumentum in $115 Billion All-Stock Deal, Betting on Optical Connectivity for AI Data Centers

Marvell and Lumentum announced on November 2 a definitive agreement for Marvell to acquire Lumentum in an all-stock deal: 4.5 Marvell shares per Lumentum share, an implied enterprise value of $115 billion (about $1,132 per share, a roughly 23.9% premium). Marvell holders will own about 67% of the combined company. The companies say AI clusters are shifting from copper to optics, and the combination will cover connectivity from data center interconnect down to in-package links, targeting a $550 billion market. Closing is expected in the first half of 2028.

Marvell to Acquire Lumentum in $115 Billion All-Stock Deal, Betting on Optical Connectivity for AI Data Centers

Key Takeaways

  • Fixed exchange ratio of 4.5 to 1 implies a $115 billion enterprise value for Lumentum, a roughly 23.9% premium to the unaffected October 28 close.
  • Marvell holders will own about 67% and Lumentum holders about 33%; Matt Murphy stays chairman and CEO, and three Lumentum directors including CEO Michael Hurlston join the board.
  • The deal combines Marvell electrical and optical interconnect, switching and custom XPU silicon with Lumentum lasers, optical transceivers, optical circuit switching (OCS) and indium phosphide (InP) manufacturing.
  • The companies cite more than $1 billion in run-rate cost synergies and year-one non-GAAP EPS accretion; shareholder and regulatory approvals are required, with closing expected in the first half of 2028.
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South Korea Nearly Doubles 2027 AI and Chip Spending to 21.3 Trillion Won, With 4.7 Trillion for a Sovereign Frontier Model and 10,000 Vera Rubin GPUs

In his 2027 budget address to the National Assembly on November 2, South Korean President Lee Jae Myung proposed a record 820.9 trillion won budget (+12.8%), with 21.3 trillion won (about $16B) for semiconductors, physical AI, AI data centers and AI, nearly double the 10.8 trillion won in 2026. The Ministry of Planning and Budget document allocates 4.7 trillion won to a sovereign AI model aiming for world-leading performance, including the purchase of 10,000 Vera Rubin GPUs. Parliament still has to pass the budget.

South Korea Nearly Doubles 2027 AI and Chip Spending to 21.3 Trillion Won, With 4.7 Trillion for a Sovereign Frontier Model and 10,000 Vera Rubin GPUs

Key Takeaways

  • The three mega-projects plus AI rise from 10.8 to 21.3 trillion won, up 97.2%; the total budget is 820.9 trillion won, up 12.8% (official budget document).
  • The 4.7 trillion won frontier AI program buys 10,000 Vera Rubin GPUs (3.9T won), training data (0.8T won), and recruits 20 top overseas researchers (25B won).
  • Until then, about 7,000 government-owned GPUs go first, plus about 8,000 leased from industry and academia; companies are picked by open competition and the state invests as an equity holder.
  • A new 0.25 trillion won AI for All program builds public AI agents on the domestic model that can handle ID documents, KTX train bookings and passport renewals; AI education gets 2.1T won for 3.9 million people.
  • Another 70B won funds a dedicated AI security model and 50B won subsidizes AI security subscriptions for 10,000 SMEs; physical AI rises from 0.9T to 3.1T won.
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Aramco closes deal for 49% of HUMAIN: PIF keeps 51%, about $2.6B cash in 2026, 3 GW of AI data centers targeted by 2030

Saudi Arabia's Public Investment Fund (PIF) and Aramco announced on November 1 that their deal has closed: Aramco, through a wholly owned subsidiary, takes a 49% stake in Saudi AI company HUMAIN, while PIF keeps 51% control. Besides transferring its own AI, compute and digital assets, Aramco will contribute about SAR 9.9 billion (roughly $2.6 billion) in cash to HUMAIN in 2026. HUMAIN plans to deploy 3 GW of AI data center capacity by 2030 and keeps developing ALLaM, its multimodal Arabic large language model.

Key Takeaways

  • Ownership: Aramco 49% (through a wholly owned subsidiary) and PIF 51%; the deal closed on 2026-11-01 (Aramco press release)
  • Money: about SAR 9.9 billion (roughly $2.6 billion) in cash during 2026, plus AI, compute and digital assets (official figures)
  • Compute goal: 3 GW of AI data center capacity by 2030 (official figure)
  • Aramco says it has realized $11.3 billion in "Technology Realized Value" since 2023, much of it from AI solutions (CEO Amin Nasser, company figure)
  • HUMAIN launched in May 2025 and works in four areas: data centers, cloud platforms, the ALLaM models and industry solutions
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X discussion: big-tech CEOs talk openly about RSI - Google's Sundar Pichai says "we are pretty far from the end state of RSI" yet expects "continued acceleration because of RSI" over the next 12 months, while Sam Altman and Dario Amodei weigh in

Recursive self-improvement (RSI), using models to speed up the development of the next models, became one of the most-used terms in AI circles on X this past week after several big-tech CEOs talked about it in public. Google CEO Sundar Pichai framed it differently on two podcasts. On Alex Kantrowitz's Big Technology Podcast he said "We are pretty far from the end state of RSI." On Alex Heath's Sources podcast he said researchers are co-working with models "in a deep way" and "I would expect the next 12 months to see continued acceleration because of RSI." The widely shared 12-month line comes from the Sources episode, not Big Technology. OpenAI CEO Sam Altman told Stanford GSB that besides AI recursive self-improvement there is another loop, robots that build robots and data centers that build data centers, that gets "much less talk". Anthropic CEO Dario Amodei, replying to a critic on X, wrote about the point where AI-driven acceleration gets fast enough that safety struggles to keep up: "We are nearing that point." This is an X discussion of public remarks by CEOs, not an announcement of any new research program.

Key Takeaways

  • On the Big Technology Podcast (released Oct 20), Pichai said "We are pretty far from the end state of RSI", that today there are "RSI loops along with AI researchers", and that he expects that continuum to keep progressing into next year (Big Technology recap; Kantrowitz post, 68 likes and 9,643 views when we pulled it)
  • On the Sources podcast (released Oct 21, from about 11:52), Pichai said researchers co-work with models "in a deep way", with models suggesting recipes and automatically evaluating next checkpoints, and added "I would expect the next 12 months to see continued acceleration because of RSI" (Alex Heath episode post, 253 likes and 388K views when we pulled it)
  • Reposts that place the 12-month line in the Big Technology interview are wrong; after checking transcripts of both shows we found it in the Sources episode. The early repost from @ONagel33303 had only 4 likes, and its "CONFIRMED: Google plans to accelerate" framing is the reposter's own reading
  • At Stanford GSB's View From The Top (video released Oct 21, at about 33:18), Altman described "robots that can build more robots and then data centers that can effectively build more data centers" as a loop besides AI recursive self-improvement that gets "much less talk"
  • Anthropic CEO Dario Amodei, replying to Encode AI's Nathan Calvin, wrote about the point where AI-driven acceleration gets fast enough that it is hard for safety to keep up: "We are nearing that point" (post, 4,103 likes and 1.59M views when we pulled it); Calvin and journalist Garrison Lovely then criticized Anthropic for warning about RSI while backing the AEGIS Act, which they say makes it harder for governments to stop it
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DeepSeek Scales Ascend Compute Clusters; 552B Asymmetric MoE Model Reaches Record Throughput

DeepSeek disclosed large-scale deployments of Huawei Ascend clusters across its new AI data centers. Paired with its 552B-parameter asymmetric MoE model DeepSeek-V4.1-Flash, the architecture achieved record inference throughput and memory efficiency on domestic silicon.

Key Takeaways

  • Deploys Huawei Ascend AI accelerators at scale to secure hardware sovereignty
  • 552B asymmetric MoE architecture sets new standards for inference speed and vision-code reasoning
  • Reaffirms DeepSeek global leadership in cost-performance and open-weights efficiency
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Together AI + Equinix + NVIDIA: Inference Exchange brings open models to global DCs

Together AI (@togethercompute) announces Equinix Inference Exchange with Equinix and NVIDIA: its open-model platform is live across Equinix’s global data centers, productizing both which model enterprises run and where it runs — arguing model choice and performance are not a trade-off.

Key Takeaways

  • Equinix Inference Exchange launches Together’s open-model platform across Equinix global DCs.
  • NVIDIA-backed enterprise inference path tying model choice to where workloads run.
  • Positions open source as the default enterprise AI build path without sacrificing performance.
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Together AI and HUMAIN sign 250MW open-source data center deal, $5B+ annualized

Together AI said it signed one of the largest AI infrastructure deals for open source: a 250MW data center with HUMAIN in Saudi Arabia, carrying more than $5 billion in annualized revenue. The company framed the pact as open-source compute catching up with closed models that have long been bankrolled by hyperscalers, and argued power—not chips alone—is the binding constraint. For teams that fine-tune and serve open weights, the deal points to a step-change in dedicated capacity without each lab raising its own multi-billion-dollar balance sheet.

Key Takeaways

  • The deal is a 250MW data center with HUMAIN in Saudi Arabia and more than $5B in annualized revenue.
  • Together positions it as open-source compute catching up with hyperscaler-backed closed models.
  • The company says power is the bottleneck and partnerships beat every lab raising its own billions.
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