Capital · United States

Flex Secures $2B Investment for Axiom at $37.5B Enterprise Value

Credited to Converge Digest · convergedigest.com · 2d ago

Flex has secured a $2.0 billion convertible preferred equity investment in Axiom Solutions International, establishing an initial enterprise value of $37.5 billion for the AI infrastructure business that Flex plans to spin off as an independent public company in the first quarter of 2027. Funds affiliated with General Catalyst, Koch Equity Development and co-investors are purchasing the Series A Convertible Preferred Stock.

The financing provides equity capital for Axiom’s pending acquisition of EPC Power, the $4.4 billion transaction Flex announced in September to expand its grid-to-chip power portfolio. Flex has separately arranged committed term-loan financing for the remainder of the EPC Power acquisition. The preferred shares carry a 10% annual cash dividend before separation; afterward, 6% cash or 7% PIK.

The investment adds an external valuation benchmark to a business Flex has assembled around high-density AI infrastructure. Axiom will combine embedded and critical power, electrical infrastructure, power conversion, thermal management and compute integration. Flex continues to target completion of the Axiom separation in Q1 calendar 2027, subject to required approvals and customary conditions.

Investors — General Catalyst, Koch Equity Development and co-investors

The $37.5 billion enterprise value is the most consequential new data point in today’s announcement. When Flex formally named Axiom in September, the preliminary Form 10 established operating scale but did not provide this external investment valuation.

The financing clarifies how Flex intends to fund the next major expansion of Axiom. The pending $4.4 billion EPC Power acquisition moves the portfolio farther upstream into MW-scale power conversion, grid-forming controls, energy storage and emerging 800 VDC architectures.

The financing should not be treated as completion of either the EPC Power acquisition or the Axiom separation. Both remain subject to applicable approvals and closing conditions.

Company Profile • Axiom Solutions International

Flex Names $7B Infrastructure Spin-Off Axiom — Sept. 16, 2026

Flex Makes $4.4B Bet on 800V AI Data Center Power — Sept. 3, 2026

Flex to Acquire EP² for $1.1B — Mar. 30, 2026

Flex Debuts 33 kW Power Shelf for NVIDIA Blackwell — Aug. 5, 2025

CapitalUnited States

AOI Raises $588M to Fund Manufacturing Expansion

Credited to Converge Digest · convergedigest.com2d ago

Useful

Applied Optoelectronics ( NASDAQ: AAOI ) has completed a previously announced $600 million at-the-market equity offering program, raising approximately $588 million in net proceeds as the optical component manufacturer expands production capacity for high-speed data center connectivity. AOI sold 5,694,845 common shares at an average price of $105.36 per share. The offering, first disclosed on August 21, represents the company’s third ATM equity program completed this year.

The new capital arrives as AOI scales manufacturing for 800G and 1.6T optical transceivers targeting hyperscale and AI data center networks. AOI said proceeds may be used for general corporate purposes including debt repayment, working capital and capital expenditures, with manufacturing expansion for data center optical devices identified as a primary focus. CEO Dr. Thompson Lin said the company plans investments in new production and R&D equipment as it expands advanced manufacturing capacity.

AOI’s manufacturing buildout is already substantial. In July, the company began developing nearly 400,000 additional square feet of manufacturing capacity at two properties in Pearland, Texas, focused on 800G and 1.6T transceivers. Earlier this year, AOI also secured a volume order exceeding $200 million for 1.6T transceivers from a major hyperscale customer. The company has been expanding capacity in both Texas and Taiwan as AI clusters drive higher optical bandwidth requirements.

Net proceeds — $587,999,985.89 after commissions and fees

Program — AOI’s third ATM equity program of 2026

Primary investment — Manufacturing expansion for data center optical devices

Other uses — Debt repayment, working capital, production equipment and R&D equipment

“We intend to use these proceeds to continue to make investments in the business, including new equipment and machinery for production and research and development use, as we continue to scale our advanced manufacturing capacity,” said Dr. Thompson Lin, Founder, President and CEO of AOI.

The significance of the transaction extends beyond the size of the equity raise. AOI is converting strong capital-market access into manufacturing capacity at the same time that its data center portfolio is moving from 400G toward much higher volumes of 800G and 1.6T products.

That relationship between capital and capacity is particularly visible in Texas. AOI began its Pearland expansion in July, adding nearly 400,000 square feet for high-speed transceiver manufacturing. Demand provides the other side of the equation: AOI disclosed in March that it had secured a 1.6T order worth more than $200 million from a long-standing hyperscale customer.

The $588 million infusion therefore gives AOI substantially more financial capacity to execute its manufacturing roadmap. The remaining question is how quickly the company can translate those investments into qualified production capacity and shipments as hyperscalers migrate toward 800G and 1.6T connectivity.

Applied Optoelectronics (AOI) — Company Profile

Leadership — Dr. Thompson Lin — Founder, President & CEO

AI Optics — 400G, 800G and 1.6T optical transceivers; external laser technology and optical components

Recent Milestone — October 2026: Completed $600M ATM program, generating approximately $588M net

Related Converge Digest Coverage — Applied Optoelectronics

SEPTEMBER 30, 2026 · OPTICAL AOI Debuts 25G Tunable DWDM SFP AOI expands its transport-optics portfolio while marking its millionth 1.8 GHz HFC amplifier shipment.

AUGUST 9, 2026 · CORPORATE STRATEGIES AOI’s 800G Ramp Accelerates as U.S. Manufacturing Gains Strategic Weight Q2 analysis examines AOI’s accelerating 800G ramp, capacity constraints and expanding Texas manufacturing footprint.

JULY 14, 2026 · OPTICAL AOI Starts Pearland Factory Buildout for 800G and 1.6T Optics Nearly 400,000 square feet of additional Texas manufacturing capacity targets next-generation data center optics.

APRIL 29, 2026 · CORPORATE STRATEGIES AOI Lands $20.9M Texas Grant to Scale Transceiver Manufacturing Texas Semiconductor Innovation Fund support backs AOI’s domestic high-speed transceiver manufacturing expansion.

MARCH 9, 2026 · OPTICAL AOI Lands $200M Hyperscaler Order for Transceivers AOI’s first major 1.6T volume order established the demand backdrop for its current manufacturing expansion.

Original · Converge Digest

Front Method

Capital · United States

The AI Boom Has a Gigawatt Accounting Problem

Credited to EE Times · eetimes.com · 1d ago

Photograph · EE Times

Track energized compute, not gigawatts, as AI data centers face delays in memory, networking, cooling, and power.

Broadcom can lend Anthropic up to $42 billion to help finance computing infrastructure. Nvidia says it intends to invest as much as $100 billion in OpenAI as Nvidia systems are deployed. AMD has issued warrants to major customers whose vesting is tied partly to gigawatt-scale GPU purchases.

Capital is clearly available for AI infrastructure. The harder question is what those commitments will produce in the physical world.

The industry increasingly describes AI infrastructure in gigawatts. OpenAI and Nvidia have announced plans for at least 10 GW of Nvidia systems. OpenAI has a separate definitive agreement with AMD covering 6 GW of GPUs and another collaboration with Broadcom covering 10 GW of OpenAI-designed accelerators and networking systems. Those figures look comparable, but they describe different things.

A gigawatt of contracted GPUs is not necessarily a gigawatt of data center capacity. Contracted cloud compute is not the same as installed hardware, and infrastructure described as secured is not necessarily infrastructure that has memory, networking, cooling, and power available to operate.

That distinction matters because AI infrastructure is becoming a systems problem, not simply a semiconductor capacity problem.

I have followed semiconductor capacity cycles for more than two decades, including during my years at Intel. Purchase orders are only one part of the capacity equation. Manufacturing, packaging, memory, and the infrastructure around the chip determine how quickly demand turns into usable systems.

Nvidia’s Vera Rubin reference design illustrates the scale involved. Its published configuration for a 100 MW AI factory includes 40,000 Rubin GPUs and 12 petabytes of HBM4. A simple linear scaling of that reference design would imply roughly 400,000 GPUs and 120 petabytes of HBM4 per gigawatt. That is an illustration, not a universal conversion factor for AI capacity.

The memory requirement alone is significant. Samsung expects HBM to account for nearly 30% of industry DRAM wafer capacity in 2027, up from about 20% currently. Samsung also notes that HBM and standard DRAM share wafer production capacity, so growth in one affects the capacity available for the other.

The next challenge is connecting the processors. Large AI clusters require switches, network-interface cards and large volumes of high-speed optical links. At rack level, rising power density adds liquid cooling, coolant distribution, large power systems, and more complex commissioning.

The GPU is therefore only one component in a much larger industrial system. The supply chain extends from semiconductor manufacturers and memory producers to cooling suppliers, electrical contractors, utilities, and power generation companies.

That becomes clear when construction starts.

JLL estimates that a 50 MW data center takes about 18 months to build globally. Developers are ordering some equipment as much as two years in advance, yet 57% of projects experienced construction delays of at least three months in 2025. In the U.S., JLL puts average equipment lead times at roughly 42 weeks, 83% above 2019 levels. Transformers and switchgear average about 43 weeks, while generators average roughly 51 weeks. A contracted gigawatt can therefore exist long before it becomes usable compute.

The difference also becomes more important closer to the power grid. Semiconductor capacity is relatively fungible. Wafers can be reallocated between customers, and some production steps can be moved between suppliers. A substation, transmission connection, or gas pipeline belongs to a specific location.

That makes power increasingly central to AI deployment. Enverus forecasts that 22.5 GW of new U.S. data center demand between 2026 and 2030 will be served by behind-the-meter generation, equivalent to about 36% of projected U.S. data center capacity additions during that period. Its forecast includes 29.6 GW of gas-fired generation.

Developers are effectively considering power generation as part of the data center project rather than waiting for conventional grid expansion. That can shorten some timelines, but it creates another set of constraints around turbines, engines, transformers, pipelines, and permits. Enverus itself cautions that access to a theoretical power source does not automatically make a site ready for construction.

The financing structures now emerging around AI make these physical constraints more relevant.

OpenAI and Nvidia’s announced partnership calls for at least 10 GW of Nvidia systems, representing millions of GPUs. Nvidia says it intends to invest up to $100 billion in OpenAI as the systems are deployed. The first gigawatt is targeted for the second half of 2026 using Vera Rubin. Importantly, the arrangement was announced as a letter of intent rather than a completed purchase obligation.

OpenAI separately entered a definitive agreement with AMD covering 6 GW of GPUs across multiple generations. The first 1 GW deployment using MI450-series products is scheduled to begin in the second half of 2026. AMD also issued OpenAI a warrant for up to 160 million AMD shares, with vesting linked in part to GPU-purchase milestones.

A separate OpenAI collaboration with Broadcom covers 10 GW of OpenAI-designed accelerators and networking systems, with deployment targeted to begin in the second half of 2026 and finish by the end of 2029.

Those announcements cannot simply be added together and treated as separate data center capacity. They describe different layers of the same infrastructure buildout.

The Meta-AMD agreement shows why that distinction matters. AMD describes the broader relationship as covering up to 6 GW, but its SEC filing says Meta made a binding commitment for the initial 1 GW. Full vesting of Meta’s warrant for up to 160 million AMD shares depends on purchases eventually reaching 6 GW. The headline number and the current binding obligation are not identical.

Anthropic provides another example of how financing and hardware are becoming intertwined.

According to Anthropic’s IPO prospectus, as reported by Reuters, Broadcom agreed to lend the company up to $42 billion to help finance infrastructure. Reuters reported that the facility could cover roughly one-third of a five-year, $125.2 billion commitment for TPU computing capacity. Anthropic is also expected to become Broadcom’s largest chip design customer in 2027.

This does not mean the underlying demand is artificial. It does mean that the roles of supplier, customer, investor, and financier are becoming increasingly difficult to separate.

The same future unit of compute can now appear in several places: as a semiconductor order, a cloud compute commitment, a data center project, a financing obligation, and eventually as capacity described by an AI company as secured.

Each disclosure may be accurate on its own. Problems arise when they are added together as though each represents separate physical capacity. That is the accounting issue behind the industry’s growing use of gigawatts. A gigawatt is a precise unit of electrical power. It is much less precise as a measure of AI capacity.

None of this means the AI infrastructure boom cannot be built. Semiconductor manufacturers are expanding production, memory suppliers are adding HBM capacity, and data center developers are ordering equipment earlier and looking for alternatives to conventional grid connections.

The more important constraint may be timing. Capital can be committed quickly. A GPU agreement can be signed years before delivery, and a cloud customer can contract for compute before the underlying facility exists. Usable compute becomes available only when the semiconductor, memory, network, rack, cooling, and power layers are ready in the same place.

A one-year delay does more than move a construction schedule. It affects utilization, financing costs, depreciation assumptions, and the timing of revenue throughout the infrastructure supply chain.

For that reason, announced gigawatts may become less useful as the AI buildout matures. A better measure may be time to energized compute: How long it takes a financial commitment to become a functioning system that can actually run workloads.

The AI industry has shown that it can finance enormous amounts of future capacity. The next test is how quickly the physical infrastructure can catch up.

AI Power Demands Push GaN into Data Center Design

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The Energy Barrier Reshaping AI Hardware

OpenAI and Nvidia Systems Partnership Supports the announced 10 GW framework, millions of GPUs, intended investment of up to $100 billion and the initial Vera Rubin deployment. https://openai.com/index/openai-nvidia-systems-partnership/

OpenAI and AMD Strategic Partnership Supports the 6 GW definitive agreement, first 1 GW MI450-series deployment and warrant for up to 160 million AMD shares. https://openai.com/index/openai-amd-strategic-partnership/

OpenAI and Broadcom Strategic Collaboration Supports the 10 GW accelerator and networking collaboration and targeted 2026-2029 deployment period. https://openai.com/index/openai-and-broadcom-announce-strategic-collaboration/

Nvidia Vera Rubin NVL72 Supports the 100 MW reference configuration with 40,000 Rubin GPUs and 12 PB of HBM4. The 1 GW figures in the article are derived by simple linear scaling for illustration only. https://www.nvidia.com/en-us/data-center/vera-rubin-nvl72/

AMD SEC filing, Meta Agreement Supports the binding initial 1 GW commitment, broader arrangement up to 6 GW and warrant for up to 160 million AMD shares. https://www.sec.gov/Archives/edgar/data/2488/000000248826000045/amd-20260223.htm

Reuters, Broadcom, and Anthropic Supports the financing facility of up to $42 billion, the $125.2 billion five-year TPU compute commitment and expected 2027 customer relationship. Reuters

Reuters, Samsung HBM capacity Supports Samsung’s expectation that HBM will approach 30% of industry DRAM wafer capacity in 2027, versus about 20% currently. Reuters

JLL, Global Data Center Outlook 2026 Supports construction timelines, 2025 project delays and U.S. equipment lead-time estimates. https://www.jll.com/content/dam/jllcom/en/global/documents/reports/research-reports/26-research-global-data-center-outlook-new.pdf

Enverus, behind-the-meter generation forecast Supports the forecast for 22.5 GW of U.S. data center demand served behind the meter, the 36% share of capacity additions and the modeled gas-generation requirement. https://www.enverus.com/newsroom/behind-the-meter-generation-forecast-skipping-the-queue/

Enverus, site readiness Supports the discussion of pipeline, permitting and infrastructure constraints on data center sites. https://www.enverus.com/blog/data-center-sites-powered-by-gas-isnt-the-same-as-ready-to-build/

RELATED TOPICS: AI AND BIG DATA, POWER MANAGEMENT

Ron Honig is co-CEO of From Honig Family Office. He previously spent more than two decades in the technology industry, including at Intel, with a long-standing focus on the semiconductor sector.