From Suppliers to Shareholders: The Big Three Memory Chip Giants Jointly Invest in Anthropic, AI Supply Chain Power Structure Undergoing Reshuffle

marsbitPubblicato 2026-05-30Pubblicato ultima volta 2026-05-30

Introduzione

For the first time, memory chip giants Micron, Samsung, and SK hynix have jointly invested in the same AI company, Anthropic, as part of its massive $65 billion Series H funding round. This strategic move, positioning the three rival HBM suppliers as "strategic infrastructure partners," highlights a fundamental shift in the AI industry's power dynamics. With HBM (High Bandwidth Memory) being a critically scarce resource essential for AI model training and inference, securing a stable supply has become a key competitive differentiator. By making these chipmakers shareholders, Anthropic aims to lock in this vital component for its rapid expansion, which includes securing major compute commitments from Amazon, Google, and others. For the memory trio, this investment represents a strategic bet on defining the future of AI hardware. Each company gains: SK hynix reinforces its dominant position in the NVIDIA supply chain; Samsung diversifies its client base beyond NVIDIA; and Micron leverages its geopolitical significance as the sole US-based HBM maker. Their collective move signals that competition in AI is evolving beyond model capability to encompass control over the entire compute supply chain—from chips and memory to power and networking. This vertical integration trend, where infrastructure providers become direct stakeholders in AI firms, marks the industry's maturation as AI transforms from a research project into essential global infrastructure, setting the stage for a ne...

Micron, Samsung, and SK hynix have made a historic first joint appearance on the same AI company's funding list.

On May 28 local time, Anthropic announced the completion of its Series H funding round, raising a total of $65 billion at a post-money valuation of $965 billion. This officially surpasses OpenAI's previous valuation of $852 billion, making Anthropic the world's most valuable AI company.

The scale of this funding round is staggering in itself. However, what truly made the industry stop and take notice is an unprecedented combination on the list of investors: Micron, Samsung, and SK hynix—currently the world's only three manufacturers of high-bandwidth memory (HBM)—appeared simultaneously as shareholders in the same AI company for the first time.

These three companies have historically been each other's most direct competitors, vying for the same orders from NVIDIA, AMD, and Google, fighting inch by inch for market share in each generation of HBM. Yet now, they sit at the same table, endorsing the same AI company.

The Logic of "Strategic Lock-in" for the Supply Chain

In its official announcement, Anthropic referred to Micron, Samsung, and SK hynix as "strategic infrastructure partners," not ordinary financial investors. The specific investment amounts from the three companies were not disclosed. However, being officially singled out as "strategic infrastructure partners" itself signifies a status exceeding that of most follow-on investment institutions on the list, indicating that the logic of this investment lies not in financial returns but in supply chain synergy. The official wording states: These relationships will help Anthropic "reliably scale compute capacity at the pace our customers need."

The meaning of this statement needs to be understood within the current industry context.

By 2026, HBM is no longer a commodity that can be restocked at any time; it is one of the scarcest strategic resources in the global AI arms race. The annual production capacity of the three suppliers was already fully booked in Q1 2026, with an estimated supply-demand gap between 20% and 50%. The shortage is expected to persist until 2028. SK hynix holds approximately 50% market share, while Samsung and Micron hold about 28% and 22%, respectively.

In such an environment of extreme supply constraints, whoever secures enough HBM can support the training and inference of large-scale AI models. This funding round by Anthropic has disclosed commitments including $50 billion from Amazon and a total of $150 billion from other hyperscale cloud service providers, along with locking in a new 5 GW compute agreement with Amazon, 5 GW of next-generation TPU compute from Google and Broadcom, and access rights to the SpaceX Colossus GPU cluster.

The issue of compute supply has a preliminary solution, but HBM, the core raw material for compute, remains a bottleneck. Having the three memory chip giants invest simultaneously essentially builds a competitive barrier at the supply chain level: not paying for goods, but making upstream manufacturers direct stakeholders in a community of interest.

Why the Three Giants Are Willing to Place a Joint Bet

From the perspective of the three companies, this investment also aligns with their respective strategic logic.

SK hynix is the primary supplier of HBM4 for NVIDIA's Rubin platform, accounting for about 70% of supply. HBM revenue already exceeds 50% of its total revenue, with gross margins estimated at 55% to 60%—about double that of regular DRAM. For SK hynix, deep alignment with Anthropic means establishing a stable, long-term demand anchor on the AI inference side. Anthropic's compute expansion drives cloud providers to purchase GPUs, and the bottleneck in GPU production capacity lies with HBM. This transmission chain is precisely the link where SK hynix, as NVIDIA's primary HBM4 supplier, holds the strongest control.

Samsung, between 2024 and 2025, was denied supply by NVIDIA due to HBM3E yield issues, only returning to the market in February 2026 with HBM4 mass production. Previously, Samsung secured the primary supply qualification for HBM4 on AMD's MI455X platform and captured over 60% share in Google TPU orders. Betting on Anthropic is a crucial step for Samsung in building a diversified customer portfolio in the "post-NVIDIA era."

Micron is the smallest of the three in size but possesses the most unique strategic value. As the only U.S.-based HBM manufacturer, Micron enjoys approximately $6.1 billion in subsidies under the CHIPS Act, giving it an irreplaceable "domestic attribute" in the increasingly complex geopolitical global compute landscape. Its HBM annualized revenue run rate reached $8 billion in Q4 FY2025.

For these three companies, jointly investing in Anthropic is a way to "actively participate in defining the future form of AI." The compute demand specifications of large AI models will be transmitted up the supply chain from GPUs to memory chips, directly influencing the evolution of memory architecture, bandwidth specifications, and packaging technology. Becoming a shareholder means gaining the opportunity to grasp these demand signals earlier, thereby influencing the direction of next-generation HBM specifications.

Rewriting the Underlying Ecology

Viewed in isolation, this funding round could easily be interpreted as an exceptionally large-scale venture capital investment. But when placed within the broader context of the AI industry over the past 18 months, a larger picture emerges.

Anthropic's annualized revenue surged from $30 billion in early April to $47 billion currently, in less than two months—a growth rate that insiders describe as "never seen before." Claude has become the world's first frontier AI model simultaneously available on the three major platforms: Amazon Web Services, Google Cloud, and Microsoft Azure. The explosive growth of Claude Code is reshaping the enterprise development tools market. Anthropic anticipates its first profitable quarter, which, for an AI company just a few years old, marks a new stage of business model maturity.

Simultaneously, the investment focus of the entire AI industry is shifting. A few years ago, capital mainly focused on the model capability race; now, the key variables determining the competitive landscape are increasingly concentrated at the infrastructure layer: compute, storage, networking, and power. Hyperscale cloud service providers, chip manufacturers, and energy companies are starting to enter AI company shareholder structures directly. This trend of "vertical integration" essentially reflects the supply chain being reconfigured as an ecosystem moat.

From OpenAI backed by Microsoft, to Google betting on in-house TPUs, and now the three major memory chip suppliers simultaneously investing in Anthropic, the dimensions of competition in the AI industry have expanded from "whose model is better" to "who controls a more complete compute supply chain."

This restructuring didn't happen suddenly, but it appears particularly clear at this moment in 2026: AI large models have evolved from lab products to critical production infrastructure, and the supporting upstream hardware supply chain is completing its deep integration with the model layer through equity stakes.

The Series H funding will build a broad moat for Anthropic before its IPO. Yet more noteworthy than the funding scale itself is the industrial logic revealed by this investor list: when the three memory chip giants set aside market competition and take seats as shareholders in the same AI company, they are essentially casting a vote with real money for the entire industry. AI's dependence on underlying hardware has reached a level significant enough to reshape strategic supply chain relationships.

This is not the end of AI, but the starting point for a new round of ecosystem game theory after AI becomes infrastructure. (This article was first published on the Titanium Media APP, written by | Silicon Valley Tech_news, edited by | Qin Conghui)

Domande pertinenti

QWhat is the significance of Micron, Samsung, and SK hynix all investing in Anthropic's funding round?

AIt is historically significant as it marks the first time the three major HBM manufacturers, who are direct competitors, have jointly invested in the same AI company. This indicates a strategic shift where AI infrastructure, particularly securing HBM supply, has become so critical that it is reshaping traditional competitive dynamics and supply chain relationships.

QWhat strategic purpose does the investment serve for Anthropic, beyond financial backing?

AThe primary purpose is to secure a reliable, high-priority supply of HBM (High Bandwidth Memory), the most scarce strategic resource in the AI arms race. By making the HBM suppliers strategic shareholders and partners, Anthropic aims to build a competitive moat at the supply chain level, ensuring it can scale its computing power reliably to meet demand.

QWhat are the individual strategic motivations for SK hynix, Samsung, and Micron to invest in Anthropic?

ASK hynix, as Nvidia's primary HBM4 supplier, seeks to anchor long-term demand from the AI inference side. Samsung, which faced supply issues with Nvidia previously, aims to diversify its customer base beyond Nvidia (e.g., with AMD and Google). Micron, the only US-based HBM maker, leverages its geopolitical 'local' advantage and aims to participate in defining future AI hardware needs.

QAccording to the article, what broader trend in the AI industry does this investment highlight?

AIt highlights a shift in the AI industry's competitive focus from solely 'whose model is better' to 'who can control a more complete compute supply chain.' The investment center of gravity is moving to the infrastructure layer (compute, storage, network, power), leading to vertical integration where hardware giants and cloud providers are becoming direct stakeholders in AI companies to build ecosystem moats.

QWhat milestone did Anthropic achieve with this funding round in terms of valuation?

AAnthropic achieved a post-money valuation of $965 billion, which officially surpasses OpenAI's previous valuation of $852 billion, making Anthropic the world's highest-valued artificial intelligence company.

Letture associate

Why More AI Agents Does Not Equal Higher Productivity?

Editor's Note: As AI Agents become cheaper and easier to use, a new constraint emerges: the cost isn't in launching more Agents, but in the human attention required to manage, judge, and integrate their outputs. This hidden cost is called the "orchestration tax." The article argues that a developer's cognitive bandwidth is the key bottleneck—a serial, non-parallelizable resource akin to a Global Interpreter Lock (GIL). While many Agents can run concurrently, their results ultimately require human judgment for review, conflict resolution, and final integration. Therefore, more Agents don't automatically mean higher productivity; they can simply create longer queues, lead to cognitive fatigue, and create the illusion of busyness without real output. The core solution is to design workflows around this scarce human attention. Key strategies include: scaling the number of Agents to match review capacity (not UI capacity), categorizing tasks (delegating independent ones, keeping complex judgment-heavy ones serial), batch reviewing results to minimize context-switching costs, automating verifiable checks to reserve human judgment for critical decisions, and protecting focused, uninterrupted thinking time. Ultimately, the critical skill is not launching many Agents, but architecting systems that respect the fundamental limit of human attention. Unpaid "orchestration tax" accumulates as both technical and cognitive debt, undermining system understanding and quality. True productivity comes from thoughtfully managing the single-threaded resource—your focus.

marsbit1 h fa

Why More AI Agents Does Not Equal Higher Productivity?

marsbit1 h fa

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

Three Years Later: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's launch, I made 20 predictions about its future. Now, in mid-2026, I've used AI agents to fact-check each one against the latest data. Overall, most major directional forecasts were correct, with only one outright error (incorrectly stating GPT-4 had 100 trillion parameters). Key successes included predicting that RAG and retrieval architectures would become the standard for handling knowledge and hallucinations, that natural language interfaces (LUI) would create a massive new industry layer beyond the models themselves, and that China would develop viable large language models, significantly closing the performance gap with Western counterparts within about three years. Predictions about the absence of mass unemployment, the rise of a new "robot network" for agent communication, and ChatGPT not possessing consciousness also held true in their core arguments. However, the "devil was in the details." Errors frequently involved specific numbers, timelines, or overlooking distributional effects. I tended to overestimate the speed of adoption (e.g., for agent networks) while underestimating the ultimate scale of capabilities or costs (e.g., AI winning IMO gold without tools, or the extreme capital required for frontier models). Other misjudgments included: underestimating how AI would reinforce, not dissolve, information filter bubbles; incorrectly assuming AI-generated content would easily circumvent copyright (it has instead triggered record-breaking settlements); and misidentifying where value would be captured (it accrued overwhelmingly to the compute layer, like Nvidia, not just the application or model layers). Key lessons from reviewing these predictions are: 1) Directional and mechanistic insights are far more reliable than precise numbers or absolute statements. 2) There's a consistent bias to overestimate short-term speed but underestimate long-term magnitude. 3) Errors often lie in missing distributional impacts within a generally correct aggregate trend. 4) Predictions phrased with nuance and caveats aged the best. 5) Some fundamental debates (e.g., on machine consciousness or the ultimate value chain) remain unresolved even after three years. This exercise is less about scoring the past and more about establishing rules for clearer thinking about the next three years of AI.

marsbit7 h fa

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

marsbit7 h fa

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

Looking Back After Three Years: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's debut and before GPT-4's release, I made over twenty predictions about AI's future based on limited information and intuition. Now, in May 2026, I revisited those forecasts using an AI-driven analysis with 41 Opus 4.8 agents to cross-reference them with the latest data. The assessment used symbols: ✅ Correct, 🟢 Mostly Correct, 🟡 Partially Correct, ❌ Incorrect. Overall, the directional judgments held up well, with only one major factual error regarding GPT-4's rumored parameter size (incorrectly cited as 100T). However, nuances and degrees of accuracy revealed more. **What Was Largely Correct:** Predictions about mechanisms and directions proved accurate. The rise of RAG (Retrieval-Augmented Generation) as the standard architecture for combating AI hallucination was confirmed, as was the transformative potential of LUI (Language User Interface) in creating a new industry layer atop GUIs. The emergence of "robot networks" (agent-to-agent communication protocols) and China's rapid catch-up in developing capable large models (closing the performance gap with top models to ~2.7%) were also on point. The analysis affirmed that LLMs lack consciousness and that the Turing Test merely measures perceived intelligence. **What Was Off Target:** Errors often involved specific numbers, over-optimistic timelines, or misjudged distributions. The prediction that value would primarily accrue to the application layer was half-right but missed NVIDIA's dominance as the profitable infrastructure layer. Forecasts about AI circumventing copyright issues and fostering a "global common ground" by averaging human viewpoints were incorrect; instead, major copyright settlements occurred and AI personalization is increasing. Estimates for model training costs ("$5-10 billion cap") were significantly off, underestimating frontier costs and overestimating replication costs. The notion that LLMs could never do complex math without tools was disproven by later models winning IMO gold. **Key Patterns from the Review:** 1. **Direction over precision:** Judgments about mechanisms and trends were more reliable than specific numbers or definitive statements. 2. **Timing bias:** There was a tendency to overestimate short-term speed but underestimate long-term magnitude and transformation. 3. **The distribution blind spot:** Aggregate-level correctness often masked uneven impacts (e.g., on young professionals' employment). 4. **The value of qualifiers:** Predictions framed with caution (e.g., "reportedly," "for now," "prototype in 2-3 years") aged better. 5. **Some debates continue:** Issues like the nature of "emergent abilities" or machine consciousness remain unresolved. This three-year review highlights that while seeing the big picture is crucial, humility regarding specifics, timelines, and disparate impacts is essential for future forecasting.

链捕手10 h fa

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

链捕手10 h fa

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

The article issues a stark warning about a potential AI investment bubble. It notes that while the AI boom shares similarities with the TMT bubble of the late 1990s, its scale is vastly larger, currently driving 93% of U.S. GDP growth. Major hyperscale cloud providers like Microsoft, Alphabet, Amazon, Meta, and Oracle are planning to invest trillions in AI data centers over the coming years. However, calculations based on analyst projections for 2025-2030 reveal a concerning math problem: expected capital expenditure growth far outpaces projected revenue growth. Even under an extremely optimistic scenario of zero costs, the implied return on investment for most of these tech giants (except Amazon) is deeply negative. This suggests that the current trajectory could lead to one of history's largest shareholder value destruction events. The piece outlines two potential escapes: AI generating vastly more revenue than currently anticipated—a near-impossible task—or a significant cutback in the planned investment splurge. The latter scenario could trigger a domino effect, severely impacting the entire tech supply chain (from Nvidia to TSMC), potentially pushing the U.S. economy into recession, and causing a major stock market downturn. The author suggests upcoming high-profile IPOs by companies like OpenAI and Anthropic might represent a transfer of risk from early investors to public market participants. While the peak of the hype cycle might sustain investment through 2026, the fundamental financial dilemma remains unresolved, setting the stage for a potential market correction in 2027 or 2028, similar to the years following Alan Greenspan's "irrational exuberance" warning.

marsbit11 h fa

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

marsbit11 h fa

Trading

Spot
Futures

Articoli Popolari

Come comprare CHIP

Benvenuto in HTX.com! Abbiamo reso l'acquisto di USD.AI (CHIP) semplice e conveniente. Segui la nostra guida passo passo per intraprendere il tuo viaggio nel mondo delle criptovalute.Step 1: Crea il tuo Account HTXUsa la tua email o numero di telefono per registrarti il tuo account gratuito su HTX. Vivi un'esperienza facile e sblocca tutte le funzionalità,Crea il mio accountStep 2: Vai in Acquista crypto e seleziona il tuo metodo di pagamentoCarta di credito/debito: utilizza la tua Visa o Mastercard per acquistare immediatamente USD.AICHIP.Bilancio: Usa i fondi dal bilancio del tuo account HTX per fare trading senza problemi.Terze parti: abbiamo aggiunto metodi di pagamento molto utilizzati come Google Pay e Apple Pay per maggiore comodità.P2P: Fai trading direttamente con altri utenti HTX.Over-the-Counter (OTC): Offriamo servizi su misura e tassi di cambio competitivi per i trader.Step 3: Conserva USD.AI (CHIP)Dopo aver acquistato USD.AI (CHIP), conserva nel tuo account HTX. In alternativa, puoi inviare tramite trasferimento blockchain o scambiare per altre criptovalute.Step 4: Scambia USD.AI (CHIP)Scambia facilmente USD.AI (CHIP) nel mercato spot di HTX. Accedi al tuo account, seleziona la tua coppia di trading, esegui le tue operazioni e monitora in tempo reale. Offriamo un'esperienza user-friendly sia per chi ha appena iniziato che per i trader più esperti.

202 Totale visualizzazioniPubblicato il 2026.04.21Aggiornato il 2026.04.21

Come comprare CHIP

Discussioni

Benvenuto nella Community HTX. Qui puoi rimanere informato sugli ultimi sviluppi della piattaforma e accedere ad approfondimenti esperti sul mercato. Le opinioni degli utenti sul prezzo di CHIP CHIP sono presentate come di seguito.

活动图片