Last week, Bloomberg reported that Meta is building a cloud infra biz to rent out compute externally. One headline knocked down AI hardware names and Neocloud, a major supplier to Meta, as the market ran with an 'excess compute' narrative. Pessimism spread, and even storage, which had been the strongest on fundamentals lately, sold off on fear.

First, our take. Dolphin Research believes:

(1) Judging by that day’s drawdown, the move looked like an overreaction driven by crowded longs getting unwound. It resembled a chain reaction once some bear theses started to get validated.

(2) However, this does not mean the market will snap back right away. When the industry narrative fractures near highs, it takes time to digest, or it needs fresh positives to offset gloom and restore confidence. Speculative money tends to pull back, and sidelined bulls will likely wait rather than rush to buy the dip.

(3) As for Meta renting compute, whether it is a temporary business choice or a planned long-term biz, it is clearly a net positive for Meta. Near term it could aid a P/E sentiment reset, but a true inflection still hinges on progress in its internal frontier models and Meta AI.

We still think frequent shifts in org structure and strategy make a full fundamental turnaround at Meta unlikely in the near term (e.g., within this year).

(4) How much fundamental uplift compute rental can deliver depends entirely on how much 'idle compute' Meta has, which may change with strategy and the broader compute landscape. Dolphin Research ran an estimate under certain assumptions for reference only.

1. From buyer to reseller — Meta stuck with excess?

End-demand Capex is the sole pillar for the compute supply chain, and only a handful of mega-cap techs have the balance sheet to sustain it. Since 2H last year, Meta, Google, and Amazon have raised funds through various channels, sparking worries about when the 'landlords run out of grain.' This expectation has become a valuation overhang for the compute chain and resurfaces periodically.

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Meta is a first-tier global buyer of AI compute alongside Google, Microsoft, and Amazon, each owning or leasing multi-GW data centers. By contribution, Meta’s 2026 Capex budget of 1450亿 accounts for over 17% of the global total.

Source: Company data, Bernstein, Dolphin Research estimate

Over the past two years, Meta stockpiled large volumes of H100/200s, plus some Blackwell and AMD MI300X. By end-2025, total equivalent compute is about 2.5 mn H100s, or ~2 GW. But many of these H100/200s are used for inference; for training very large, long-context, multimodal models, their economic efficiency is now relatively low.

From an optimal allocation perspective, given the high rental premium on older H100/200-class GPUs and Meta’s inability to use them to advance next-gen Muse Spark training, renting them out to recycle cash makes sense. It helps redeploy capital to frontier compute where ROI is higher.

Source: Company reports, Dolphin Research

Still, switching from the most aggressive buyer to a seller invites a sensitive market to read this as industry-wide 'excess compute.' At last month’s AGM, Mark Zuckerberg noted many customers approached Meta to rent compute at high prices, and if there is genuine overbuild or idle capacity, Meta would consider leasing it out.

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Back then, the market reaction was muted. After all, in Q1 Meta had just raised its 2026 Capex guidance (midpoint +100亿 to 1350亿), signaling strong appetite for compute and no obvious strategy shift. At the same time, Google reportedly curtailed compute supply to Meta, and Meta in Jun locked in 1.6 GW of long-term supply with Crosue.

Since the Q1 print, negative headlines have piled up over the past two months. Beyond slipping in the tech race hurting morale (Muse Spark in Apr looked closer to Tier 1, but now appears to have fallen behind again), the key issue is culture — frequent shifts in strategy and org design have left teams confused and unfocused.

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The timing of this leak likely ties to internal chaos in the in-house R&D stack. While Meta lags at the frontier and cannot rejoin the first tier immediately, it is hard to foresee a step-change in Meta AI user experience or broad rollout of Meta Business Agents and Meta AI bots in the near term.

Compute rentals provide a direct AI monetization path for Meta. That could ease concerns about ROI on heavy Capex and allay fears of further deterioration in 2027 profit and cash flow.

On top of that, xAI’s latest multi‑tens‑of‑bn contracts show outsized near-term premiums for instant delivery. By our math, annualized revenue per GW exceeds $30 bn, or 2–3x typical industry pricing, implying payback in roughly 1.5 years vs. B300 system deployment costs of $40–50 bn/GW. With such marked-up compute ROI, it is hard for Meta not to be tempted.

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2. Meta won’t exit the battlefield

Dolphin Research believes Meta, like xAI, is not exiting the model race. Selling idle compute while focusing spend on frontier capacity ≠ reducing total compute investment.

As we noted in our SpaceX deep dive, xAI runs two clusters — Colossus 1 (H100-centric) and Colossus 2 (GB-series). Colossus 1 is now fully leased to Anthropic, while Colossus 2 continues to train Grok 5 and future frontier models, with only part of it leased out.

By analogy, Meta’s external rentals are reportedly centered on H100/H200 stockpiled in prior years, while frontier compute like GB-series and Rubin stays focused on training core models such as Muse Spark. That preserves training velocity where it matters most.

Public data and industry forecasts suggest Meta has the world’s largest aggregate AI‑related data center capacity. By 2027, institutions project Meta will have 10 GW of total compute (self-built + external).

Source: Company data, MS, Dolphin Research

(1) Self-built capacity: ~2 GW by end-2025 (equiv. 2.5 mn H100s), with Hyperion adding another 2 GW and 4 GW in 2026/27. In total, Meta’s owned capacity could reach ~8 GW by end-2027.

Source: China IDC Circle, Meta, Dolphin Research

(2) Leased capacity: Since early 2024, Meta is estimated to have signed an aggregate 10 GW of contracts with third‑party cloud providers, primarily CoreWeave, Nebius, and Google. SemiAnalysis estimates Meta signed over 5 GW of new third‑party managed compute in 1H26 on multi‑year lock‑ups.

Source: Public information, Dolphin Research

While CoreWeave’s strong terms help ensure near-term contract performance, over the long run Neocloud will inevitably face competition from former mega buyers turning into compute lessors. That shifts the balance of power in the market.

Thus, while debate remains over whether Meta might pause Capex increases because of external rentals, intensifying competition in compute leasing will likely weigh on Neocloud’s thesis and multiple.

3. How much can Meta capture?

Back to Meta. Whether it sells compute short term or prepares for longer‑term sales, the announcement alone reduces uncertainty and should support a dual recovery in EPS and the multiple.

Per Bloomberg, Meta could operate under two models: an AWS Bedrock‑like service bundling compute plus models, or a Neocloud‑style bare‑metal rental (given Meta’s models lack clear advantages). The go‑to‑market path matters for timing and margin realization.

In today’s seller’s market, rental revenue depends on how much 'idle compute' Meta is willing to release. If it moves quickly to sell capacity in 2H this year, we think it will most likely start with bare‑compute rentals, as bundling model APIs would require scaling sales and support teams.

Active AI compute in operation is ~2–3 GW now. By 2027, as self-built capacity ramps, total reserve (self-built + external) could reach ~10 GW without adding beyond planned builds. Assuming internal model work and AI Agent plans, Dolphin Research assumes 15% and 20% of in‑operation compute is rented out in 2026 and 2027, respectively.

Given consensus already bakes in data center costs (depr., power, etc.), even using Neocloud’s 5‑yr avg. contract pricing — 100–150亿/GW annualized, far below spot — the incremental net profit lift would be sizable. Only sales, power, and platform support need to be netted out, so vs. a normal 20%–40% margin, we assume a 70% marginal margin here.

Source: BBG, Dolphin Research estimated

Under these conservative assumptions, compute rentals could add roughly 10%–15% to Meta’s net profit. META rose 9% on the rental headline but gave back ~5% the next day, indicating some correction of panic around compute. 

That said, a full fundamental turnaround still requires in‑house progress, especially faster LLM iteration to narrow the gap with Tier 1. This implies Meta’s multiple could stay under relative pressure vs. other Mag 7 for some time.

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Everyone is trying to figure out what SoFi Technologies ($SOFI) actually is—a fast-growing tech platform that deserves a massive premium, or just a modern digital bank that will eventually face traditional banking realities.

By diving into its revenue mix, core customer trends, and the recent slowdown in its technology segment, this article unpacks how SoFi is showing impressive growth in winning credit market share, while its financial engine remains heavily reliant on its traditional lending business rather than high-margin software scaling.

Key Findings

• Revenue Anchor: Lending operations remain the definitive monetization engine. Despite extensive diversification into wealth management and payments, the vast majority of net revenue is still derived from the loan platform business (LPB) and student loan recovery, validating that its core growth is fueled by credit sector expansion rather than pure SaaS adoption.
• Tech Platform Deceleration: The financial technology SaaS segment is experiencing severe top-line head-winds. Following a macro-driven migration of a top-three anchor client toward an in-house core architecture, the tech platform's revenue growth decelerated sharply to 12% in the most recent print, down from 19% in the prior quarter.
• Corporate Guidance vs. Baseline Run-Rate: Forward metrics reflect highly aggressive execution assumptions. Management’s formal 2026 framework guides for a minimum 30% member expansion, 30% adjusted top-line growth ($4.65B), and a 34% adjusted EBITDA margin ($1.6B). However, assuming a long-term CAGR normalization to 15% post-2027 reflects conservative adjustments for shifting credit supply dynamics.
• Accounting and Dilution Blemishes: The quality of historical GAAP earnings faces persistent structural dilution. Prior valuation surges to a peak of $42B market cap (exceeding 30x forward EV/EBITDA) were heavily counter-balanced by subsequent secondary equity offerings and balance sheet complexities, anchoring current mid-term valuations to an adjusted P/E of 29x or P/EBITDA of 14x under guided metrics.

Below is the comprehensive 8,000-word granular decomposition examining SoFi’s micro-financial architecture, balance sheet quality, and competitive dynamics within the broader digital banking landscape.

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I. Massive TAM is not the bottleneck

SoFi spreads across lending, brokerage, payments, crypto (stablecoins), and fintech SaaS. Yet the revenue mix shows a clear strategy: monetization rests on lending, while the other verticals primarily enhance features, stickiness, and user reach.

Thus, for growth headroom, the relevant TAM is chiefly in credit. Any upside from ancillary lines should be treated, at best, as call options under optimistic scenarios.

Given industry trends and SoFi’s edge, its reachable credit markets are mainly student loans and personal loans, with personal lending offering more durable runway. This is not only because of the secular drivers in PL, but also SoFi’s positioning.

1) Student loans: policy tailwinds linger in the near to mid term

Student loans are SoFi’s home turf, especially refi after graduation. SoFi holds ~40% share in private refi (1P + 3P combined, of an estimated $37bn private refi balance in 2025).

Private share should rise on three policy shifts:

a. Federal student loan repayment and interest resumed in 2023.

b. The SAVE plan (lower payment thresholds, interest relief) ends in Aug 2025.

c. From Jul 2026, the OBBBA act eliminates Grad PLUS and tightens federal graduate loan caps.

These changes raise repayment pressure on existing borrowers, boosting refi demand and shifting incremental originations to private lenders. With total SL outstandings growing slowly (tuition inflation slowing), the private SL market should expand from the ~$140-150bn range rather than stay flat.

Using Motor Intelligence’s outlook, we assume:

(1) U.S. SL outstandings grow at a 3% CAGR over the next five years, slower than EM but faster than markets with mature subsidies and penetration (Japan, Korea, Australia).

(2) Private lenders’ share rises from ~8% today to 10%+ by 2030.

Combining (1)-(2), private SL outstandings reach ~$214bn by 2030, a 5Y CAGR of 7-8%.

2) Personal loans: structural growth drivers

The U.S. PL market is ~$280bn in balances, focused on unsecured terms of 3 months to <5 years, with 3-5 years most common. Use cases include home improvement and medical, but a key one is consolidating revolving debt (mostly credit cards).

Given unsecured risk, PL rates are high and skew toward sub-Prime to near-Prime borrowers, where fintechs are more active. While funding cycles can swing originations, the bigger driver is end-user demand for debt consolidation.

Near-term, PL volumes are rate-cycle sensitive. Early in a hiking cycle, card APRs reprice fast but PLs, often fixed-rate, lag, widening the spread and motivating refi from cards into PLs — seen in the 2022 upcycle.

Mid-cycle, PL rates catch up, narrowing the spread and dampening demand — seen in 2023-2024 as growth slowed. When cuts begin or are expected, card APRs also reset, spreads may narrow further, and credit risk rises into downturns, keeping lenders cautious on PL supply despite looser liquidity — hence a slower rebound from 2025 cut expectations.

But the long-term driver is clear. Card balances have surged and carry higher APRs, expanding demand to refi into lower-rate PLs (~10-15% vs. ~20% on cards). This is strongest among Prime+ borrowers.

PL models offer lower rates only to higher-credit borrowers, enabling the arbitrage. Prime+ also holds larger card lines, so their refi balances are higher in absolute terms.

This overlaps with SoFi’s core audience of higher-credit users. Versus peers, SoFi should benefit more as card-refi use cases expand.

Today 20-40% of PL demand is for card consolidation, based on borrower surveys and platform disclosures. LendingTree indicates over half of users borrow to pay down debt, primarily cards.

SoFi’s borrower survey shows ~1/4 cite consolidation as the top reason, and ~70% of them aim at card payoff, implying ~18% card-refi within SoFi’s PL mix.

Even so, that captures under 8% of total card balances given the trillion-dollar card market. To gauge headroom, we narrow the denominator to segments with higher conversion propensity.

a) Focus on Prime+ (~80% of card borrowers, per TransUnion) where rate spread drives refi intent. b) Within Prime+, 50-60% of card balances revolve and accrue interest (vs. 75%+ market-wide, per CFPB).

Thus, the targetable card pool is ~$1.15tn*80%*55% ≈ $506bn. The numerator is PL used for card payoff at $277bn*(20-40%) ≈ $82.8bn, implying a ~16% penetration.

With the focus on convertibles, raising penetration should face few structural hurdles. Early in a new hiking cycle, card APRs reset fast while PL rates lag, supporting demand expansion.

We then sensitize the core ratio — PL for card payoff divided by Prime+ card balances — to estimate incremental PL growth under different scenarios.

3) Mortgages: no edge for SoFi, not modeled in detail

The U.S. mortgage market is vastly larger, with ~$2tn in 2025 originations alone. But SoFi lacks product breadth and risk-model depth vs. incumbent banks, and current penetration is low.

Despite recent momentum, we defer to third-party forecasts and assume total mortgage outstandings exceed $16tn by 2030 (3-4% CAGR). We do not build a detailed TAM for SoFi here.

Aggregating (1)-(3), the broad credit TAM is ~$17tn. Excluding mortgages, core PL + SL TAM is near $600bn. SoFi’s 1P loan balance (UPB) was $34.2bn in Q4 2025, or ~6% penetration of the target TAM.

Unlike traditional lenders, SoFi also built a 3P platform in 2024, originating and servicing on behalf of capital partners. These do not sit on SoFi’s BS, but count toward brand market share from an ecosystem view.

Adding $13.2bn of 3P serviced loans (reported as 'Transferred loans serviced') to the $34.2bn 1P book, total loans reach ~$47.4bn. On the 2030 TAM, penetration rises from ~6% to ~8%, with the split by sub-market as below:

II. Balanced competitiveness: room to advance, room to defend

Having framed the space, what share can SoFi ultimately reach from ~8% today. A key constraint is SoFi’s focus on higher-credit borrowers, with average FICO ~755 across the three lending lines.

The stated floor is ~680, mapping to Prime/Good and above — roughly 70% of U.S. borrowers, or ~147mn out of ~210mn with a FICO. With ~14mn members, the surface suggests ample runway.

But the true potential is narrower, shaped by strategy and competition. We focus on SL and PL.

1) Student loans: hold the line

SL is the foundation even if not the top revenue source. SoFi is the 40%+ leader in private refi (incl. serviced transfers), but only ~11% of the broader private SL market.

Moving upstream into in-school lending is hard. Underwriting is different, school certifications and preferred lists are required, co-signer workflows must be built, and disbursements cluster by semester, raising funding management demands.

The in-school market is stable and entrenched with the Dept. of Education and private incumbents like Sallie Mae and College Ave. For SoFi, defending the refi lead should suffice to ride policy-driven refi demand post-OBBBA.

We project SoFi’s 1P+3P SL balances to grow at a 15% CAGR over five years, lifting private-market share from ~10.5% to ~14%.

2) Personal loans: where the knife fight is

PL is the cash cow. 1P alone contributes nearly half of Lending segment revenue, and most 3P today is also PL. Management highlights PL as a core growth pillar.

Competitors span OneMain (OMF) in subprime/branch networks, online fintech originators like LendingClub and Upstart, and bank players like Discover and Citi.

As of 3Q25, fintechs account for 53% of unsecured PL balances originated and serviced (ex fully sold). Traditional lenders are smaller not only due to UX and digital reach, but also conservative risk appetite limiting loan sizes — fintech avg. origination around $10k vs. ~$5k at many credit unions.

Intensity is evident in direct-mail volume, the key PL acquisition channel. Per Mintel, industry PL mailers rose 28% YoY in Apr, led by OneMain, SoFi, and Upstart. SoFi’s share was the largest at ~11%.

SoFi’s push has been aggressive, with market share climbing from ~6.8% in 2023 to ~12% by end-2025, driven by LPB. The catch: PL demand skews below Prime, which diverges from SoFi’s higher-credit focus.

Only ~35% of PL borrowers are Prime+ vs. ~70% for the overall market. In balances, Prime+ rises to ~50-60% given higher lines. In the Prime+ PL niche where SoFi competes most directly, its share is already ~22%, the clear leader.

Unless SoFi deliberately lowers its credit bar, share gains will slow from here. The brand still markets to 'high-credit' users, so a loosening seems unlikely near term.

Equally, downside risk to share looks limited. Versus incumbents, SoFi offers a more flexible origination workflow and a scalable 3P platform, while vs. pure-play fintechs dependent on warehouse lines, SoFi enjoys a charter-driven funding-cost edge with a fuller user ecosystem and a high mix of direct deposits.

One more latent flywheel: SL users stick to the platform and, as they enter the workforce, convert into PL and eventually mortgage users. We model SoFi’s combined 1P+3P loan balances rising from ~$33.5bn in 2025 to ~$67bn in 2030.

That implies unsecured PL share rising from ~12% to ~18%, a 5Y CAGR of ~16%.

III. Does SoFi still carry pure tech value

Beyond data-driven underwriting, SoFi’s 'hard tech' sits in its tech platform, Galileo, a fintech SaaS provider for core infra such as card issuance workflows, payments, and account management. Peers span Affirm and Block on the digital bank side and middleware players like Marqeta and Jack Henry.

The fintech SaaS story has matured, and Galileo, while capable, remains a small contributor. Competition is intense, clients are mid-sized fintechs with rate-sensitive budgets, and with cut hopes fading and hike expectations rising, wins in larger enterprises have yet to become a core growth driver.

In Q1 2026, revenue fell 27% YoY due to the loss of Chime (insourcing). Ex-Chime, growth was ~12%, still slower than 19% in Q4 last year.

Management cited 13 new go-lives and a deal with a top-3 U.S. telecom, which may offset churn at the high end. For now, we model a 15% CAGR through 2027+, below some Street forecasts at ~20%.

IV.

SoFi screens 'rich near term, potential over time'. The market is paying for post-transition growth rather than today’s blemishes. Our long-term stance is neutral to slightly positive, leaving room for upside.

That said, a premium brings volatility risk. Near-term catalysts exist (SL policy tailwinds, early-hike card consolidation), and SoFi bolstered cash with two raises in a liquid market. Still, position with a margin of safety per your risk tolerance.

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In 2026, the AI hardware spotlight has shifted from compute to storage capacity. As U.S. AI equities wobble, MU’s print is fast becoming the new bellwether.

Micron posted results after hours on Jun 24 (FQ3 FY26, quarter ended May), delivering in style and taking up the AI infra baton. The headline is simple: all-round strength.

Key takeaways are as follows. See below.

1. Overall results: Revenue came in at $41.5bn, up 74% QoQ, marking a second straight quarter of 70%+ sequential growth. That’s far above the prior top-end guide of $34.2bn and the Street’s ~$35.4bn.

Given shipment volumes are largely locked when guidance is set, upside elasticity rests on pricing. The huge gap vs. guide and consensus shows even the industry leader can’t fully gauge how extreme demand has become.

Volume-price dynamics, plain and strong: Volumes crawled higher — DRAM up low single digits QoQ; NAND up mid single digits. Pricing went parabolic — DRAM ASPs +60%+ QoQ; NAND roughly +85%.

The read-through is clear: in this pre-historic super-cycle for memory, capacity is all that matters. Everything else is noise.

Whoever can secure spec-compliant cleanrooms wins (new build 2.5–4 years; retrofit 1.5–2.5 years). Beyond the three DRAM majors’ own intentions, the physical-world constraints have become the critical bottleneck.

GPM: Yet another high: GPM at 84.6%, a cash machine. With revenue driven almost entirely by price, marginal delivery cost is near zero, so incremental revenue converts almost fully to GP.

This quarter, incremental revenue converted to incremental GP at a 98% rate.

2. Solid near-term guide: Revenue guidance is $42.6bn; EPS guidance is $31. The beat-on-beat guide implies ~20% QoQ revenue growth, in line with the Street.

Given most incremental revenue should fall through to profit, the revenue guide largely embeds the EPS outlook. The $31 EPS guide matches the most bullish forecasts on the Street.

Pricing is inherently hard to predict right now, so management is leaving room for upside surprise. This approach tends to ratchet up the market’s optimistic scenarios.

3. Stronger long-term view: Management now expects tight supply-demand beyond 2027. Even if balance starts to ease in 2028, the timing of supply catching demand is uncertain.

Implicitly, supply-demand could be structurally tight for a long time, opening the door for a valuation re-rate.

4. The backbone for this re-pricing is the strategic long-term agreements: the first 5-year LTA was only signed last quarter. There are now 16 strategic agreements in place.

Management repeatedly emphasized what investors care about most in a structural re-rate: LTAs. The rapid ramp was enabled by the company’s U.S.-based capacity, a key driver of the premium narrative — even if SK Hynix and Samsung combined may lead on capacity and tech, MU is the scarce ‘Made in America’ darling in Trump-era policy logic.

a. Customer mix: Covers data center, consumer electronics, and auto end-markets; specifically 4 hyperscalers + 3 mid-sized customers, plus smaller auto clients.

Customer structure is diversified across tiers. The ‘U.S. capacity’ attribute is a core selling point.

b. Tenor: Typically 5-year (CY26 to YE30). Auto LTAs are generally 3-year.

c. Key terms:

• Take-or-pay; walking away constitutes a material breach.
• Major LTAs include price bands: current product price caps at CQ2 spot; a floor runs through the contract term. Others are market-based.
• Premiums for next-gen nodes (e.g., HBM4, DDR6) are left for future negotiation, not fully fixed.

d. Financial implications:

• For 4 LTAs, cumulative revenue at floor prices totals approx. $100bn.
• Signed LTAs bring ~$22bn in cash deposits and related financial commitments.
• Price floors support GPM well above any prior cycle peak.
• If fully executed, fixed- or capped-price LTAs near current CQ2 spot would account for ~40% of total revenue.
• Once all planned SCAs are executed, approx. half or more of revenue is expected under the SCA framework.

In our view, while the four LTAs only guarantee ~$100bn at floor prices (~$20bn per year), that is merely a five-year baseline. These contracts raise the trough in down-cycles with high certainty.

MU also secured ~$22bn in deposits, strengthening protection against customer reneging. While these funds cannot be used for capex, they still matter financially.

Cash is fungible within a pool, so the deposits help address the age-old memory fear: expanding in bull markets only to waste capacity in bear markets and give back profits. This improves cycle resilience.

With this granular disclosure, are memory names set for a valuation re-rate? Thus far, the group has been driven more by EPS than by multiple expansion.

4. Price surge: DRAM revenue was $31.3bn, +67% QoQ, with 60%+ from price. NAND reached $9.9bn, +99% QoQ, with ASPs up ~85%.

Such a ramp only happens when demand spikes and capacity cannot be released quickly. Pricing then does the heavy lifting.

By end-market, AI data center revenue grew 103% QoQ to $11.5bn, the largest positive surprise. Traditional cloud storage rose 78% QoQ to $13.8bn on broad-based price hikes across specs, but is about to be caught by core AI data center.

Mobile and PC posted the weakest sequential gains yet still grew nearly 50%. OEM margins in smartphones and PCs will feel the squeeze.

HBM uses larger die, complex stacking, lower yields, and heavier packaging and test. As a result, its trade ratio vs. conventional DRAM is 2.5–3x, crowding out legacy capacity and extending the mismatch in traditional products.

Despite high HBM ASPs, margins are lower. When tightness spills over into conventional DRAM and NAND, those products earn higher margins.

Memory makers have stated non-HBM DRAM carries higher margins than HBM. Some outsiders even estimate that, per wafer, conventional DRAM delivers ~2x revenue and ~3x GP vs. HBM.

For traditional products, the sustained supply-demand tension drives greater earnings elasticity. For memory makers, that upside can exceed the pure incremental benefits from HBM.

5. Extreme operating leverage: Opex barely moved compared with surging revenue, similar to unit shipment growth. R&D rose just 5% QoQ; S&M and G&A were up 18% QoQ, negligible vs. a 74% revenue surge.

With extreme price gains and full operating leverage, OP was $33.3bn with OPM at 80%. A true cash cow — even Nvidia looks up at that mountain.

6. Capex: Investors worry that aggressive capacity adds will worsen the next trough. That concern centers on a rapid capex ramp.

But the message is: don’t panic on capex. This time, hard physical constraints, not capital discipline alone, are killing the urge to expand — there’s money but nowhere to deploy fast.

This quarter, capex ex. Gov. subsidies ($0.7bn) was $7.1bn; next quarter is planned at $10bn. In FY27, quarterly capex will stay above $10bn.

For context, operating cash flow this quarter was $25.4bn — plenty of dry powder.

More than half of incremental capex will be construction — pulling forward cleanroom buildouts rather than just tool buys. MU also signed a multi-year EUV supply agreement with ASML to support 1δ and beyond.

longbridge Dolphin Research view: time for a growth multiple? That’s the crux of the debate.

Historically, memory has been a classic cyclical: bull-market profits spur capacity buildouts, only to crush utilization and margins at the trough. Hence, peak earnings often coincide with trough multiples — investors hesitate to pay up.

This time, the focal point — and the company’s relentless messaging — is on strategic LTAs. MU aims to use 3–5 year LTAs, hefty customer deposits, and profit-share-driven capex discipline to show this cycle is fundamentally different.

At its core, the stringent LTA framework converts roughly half of revenue into contracted, banded, more predictable streams. That reduces cyclicality and stabilizes revenue and cash flows.

It also upgrades a commodity-like business into a pre-ordered, pre-funded model. Think of memory as a ‘pre-fab’ good under prepaid contracts.

The ~$22bn in deposits is the masterstroke. Even if not directly used for capex, in substance it leverages customer funds to underwrite expansion.

That partly makes MU less asset-heavy. It may cap some upside at peak heat, but it lifts the floor in downturns and, as management puts it, ‘changes the business model at its core.’

When commodity assets like lithium were pitched as growth stories at peak pricing, we were cautious — a sign of industry frenzy. Here, the AI infra super-cycle argues for a different lens.

Our biggest lesson tracking the past 2–3 years: keep an open mind to non-linear tech shifts. Avoid the ‘there is nothing new under the sky’ mindset.

As recently as 1H25, even front-line leaders expected NAND destocking to take time and diverted NAND capacity to HBM.

Despite a 4x YTD move, MU still trades at ~10x on current results and guide. With broader product LTAs (e.g., for legacy NAND) and slower capacity release, a well-educated market could grant a premium — say a move from 10x to 12x, then 15x.

The print also boosts upstream AI capex plays. MU explicitly noted its EUV LTA with ASML, where EUV reduces the DUV burden of multi-patterning, etch, and alignment.

Bottom line: the training era was a compute party. In the Agentic AI era, AI infra is broadly going green.

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When OpenAI took off, it was Microsoft’s moment. With Gemini’s rise, Google Cloud surged. So here is the question: as Anthropic stands out, which cloud vendor will benefit.

Amazon, which has been a step slow in chips, model R&D, and GPU procurement, will it finally get its turn in AI. The opportunity window may be opening.

1. What does Amazon AWS’s current AI stack look like, given its perceived laggard status.

2. How material is the partnership with Anthropic for Amazon, and how durable is it.

3. What is Amazon’s true in-house chip capability.

4. How far has Amazon come on foundation models, its weakest link.

I. Is Amazon’s AI finally ramping.

1) A closer look at AWS revenue mix

In Q1 2026, Amazon’s AI annualized revenue topped $15 bn, about 10% of AWS revenue. Back in Q1 2024, management vaguely indicated low single-digit billions annualized. AWS’s ~10% AI mix still trails Azure’s 20%+, which remains meaningfully higher.

Before breaking down AI, start with AWS’s core revenue layers. The stack is classically split into IaaS, PaaS, and SaaS/other.

a. IaaS: compute chips, storage (drives), network bandwidth and other basic resources, with EC2 as the flagship. This is entry-level virtualized infrastructure rental, so margins are low.

b. PaaS/middleware: databases, data analytics, orchestration/management, and security tooling. Margins are higher at this layer.

c. SaaS apps & other: first-party or third-party software, plus vertical solutions, IoT, etc. This is the application layer and adjacent services.

AWS has long been IaaS-heavy and still is at ~60%. PaaS has risen to ~30%, while SaaS remains single-digit as a share.

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2) AI that cuts across the stack

On top of the three-layer cake sits the ‘+1’ AI revenue line, which spans across IaaS/PaaS/SaaS. Market estimates suggest AWS AI includes several parts.

a. Primarily compute rental (IaaS): unlike traditional compute, AI rentals are generally based on Trainium/Inferentia or Nvidia GPUs. Customers also skew to large AI labs and big tech, who consolidate spend and negotiate hard.

Because AI chips and storage are far more costly, and buyers have stronger bargaining power, cloud providers earn thin margins on AI IaaS. It is lower than traditional cloud IaaS margins.

b. The second major piece is Bedrock — AWS’s MaaS/TaaS (Token-as-a-Service) platform. Instead of renting bare metal, Amazon deploys frontier models and sells model APIs/tokens directly.

c. Other pieces include SageMaker and Amazon Q. SageMaker is a pre-built platform for AI/ML training, debugging, and deployment, enabling self-training or fine-tuning, plus post-training inference.

Amazon Q is an end-user-facing AI agent suite, with Developer, Business, and Connect editions. These target developers, enterprise staff, and customer service teams respectively.

Like Bedrock, neither directly rents bare hardware. They layer services above infrastructure, yielding structurally higher margins.

AWS is growing fast with margins holding up; beyond Anthropic’s usage surge, the core reasons are:

a. AWS’s AI mix is smaller, but MaaS/TaaS is a larger share of that mix. Per Semi Analysis, Bedrock contributes ~37% of AWS AI revenue, while ~80% of Azure and GCP AI revenue still comes from IaaS-only hardware rental.

b. In absolute MaaS/TaaS revenue, AWS leads at ~$5.5 bn, with Google Cloud slightly below $5 bn and Azure sub-$2 bn. Amazon’s MaaS/TaaS OPM is ~55% vs. AWS’s overall OPM below ~38% in Q1 2026.

c. Newer clouds like Oracle and CoreWeave are generally weaker on software. They mostly do ‘bare metal’ rentals with low value-add and low margins, so their MaaS/TaaS revenue is negligible vs. the Big Three.

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II. Deep ties with AI labs are a key edge

From the compute pillar, we see AWS’s AI composition and why MaaS/TaaS is becoming the main thrust. The crux of MaaS/TaaS competitiveness is twofold.

First, model depth and breadth on the platform — do you have current SOTA models, and enough variety across types and tiers. Second, relative cost and pricing for similar models vs. peer clouds — driven by in-house chips and engineering to lower unit compute cost.

The first maps to in-house model R&D and strong third-party model partnerships. The second maps to chip design and systems engineering to push down cost per token.

Amazon has emphasized a platform approach and underinvested in frontier models, with Nova roughly around Haiku 4.5 to Sonnet 4.5 by capability. It therefore relies heavily on external AI labs to bolster model supply.

In fact, most Bedrock API/token sales are third-party model-based, with Claude as the main driver today. After striking a deep collaboration with OpenAI, GPT API/token volume on Bedrock will likely rise meaningfully too.

So let’s unpack the Amazon–Anthropic partnership in detail.

1) The Anthropic partnership

Amazon’s first deep AI lab partner was Anthropic. Claude landed on Bedrock in Apr 2023, and the formal tie-up came in Sept 2023, which then evolved across three phases.

a. Sept 2023: In the initial phase, Amazon committed up to $4 bn (funded across three tranches by May 2024). In return, Anthropic named AWS its primary cloud provider, would increasingly use Trainium/Inferentia for training and inference (reportedly shifting from TPU and Nvidia GPUs), and made Claude broadly available on Bedrock.

b. Nov 2024: Amazon added another $4 bn, totaling $8 bn, and the partnership deepened into co-design across chips and models. On hardware, Anthropic began working directly with Annapurna Labs (Amazon’s chip design arm) on Trainium. On software, Claude’s kernels were optimized for Trainium and its instruction set.

During this phase, the two also unveiled Project Rainier — a mega-scale compute campus centered on Trn chips. This will be detailed later.

c. Apr 2026: Amazon invested another $5 bn, totaling $13 bn, and holds rights to invest up to $20 bn more.

Anthropic committed to spend $100 bn over 10 years on AWS and use 5 GW of Trainium capacity, including Trn2 in production and future Trn3 & Trn4.

Notably, 1 GW of compute generally maps to slightly over $10 bn in annual revenue today. The implied revenue per GW under this 5 GW commitment is much lower than that heuristic.

Partly, utilization ramps over time and won’t hit 5 GW on day one. Anthropic also noted it may use non-Trainium compute outside AWS, but the deal still suggests a meaningfully lower all-in cost for Trn vs. Nvidia GPU-based stacks.

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2) How much revenue does Anthropic drive for AWS.

As Amazon’s most important AI partner, Anthropic contributes in two ways. The larger piece is Anthropic’s own training/inference spend on AWS, and the other is Bedrock’s distribution commissions from Claude API/token sales.

Based on press data, Anthropic’s compute spend was about 1%, 3%, and 8–9% of AWS revenue for 2024, 2025, and Q1 2026, respectively. While not huge as a total, in Q1 2026 specifically, Anthropic likely accounted for 80%+ of AWS AI revenue directly.

For Bedrock’s distribution of Claude, total sales are booked by Anthropic with Bedrock as a channel. AWS recognizes only the commission on that gross, which is smaller in absolute dollars but very high margin given minimal variable cost.

Overall, the vast majority of AWS AI revenue today is directly or indirectly driven by Anthropic. Anthropic’s ARR trajectory therefore provides a strong read-through for AWS AI growth acceleration.

(Note: when customers buy Claude API/tokens on Bedrock, the underlying compute is very likely AWS. That hardware rental is captured as Anthropic’s inference spend within AWS IaaS.)

c. There is currently no Microsoft–OpenAI-style revenue sharing tied to equity between Amazon and Anthropic. The structure is more straightforwardly commercial.

d. Bottom line, most AWS AI revenue today is Anthropic-driven, so Anthropic’s ARR momentum is a key lead indicator for AWS. This linkage is unusually tight for now.

As a simple exercise, Anthropic’s ARR peaked in Mar–Apr, then its MoM growth slowed in May. As of May, ARR was about $45 bn; assuming monthly net adds trend down conservatively, ARR could reach just over $70 bn by end-2026.

Under a simplified assumption that all AWS AI revenue is Anthropic-driven, and non-AI AWS grows ~16% in 2026 (vs. ~14.4% last year), Anthropic could represent ~19% of total AWS revenue in 2026. That would lift AWS’s 2026 revenue growth to 35%+, vs. ~28% in Q1, broadly in line with our prior model.

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Microsoft once surged on tight OpenAI ties, then faded as that relationship loosened. Model makers and model distributors can be highly correlated.

Will Amazon and Anthropic replay Microsoft’s arc. We do not think so.

Both CSPs were early investors in top labs, with similar dollar checks. But there are key differences.

a. MSFT–OpenAI are more deeply equity-bound: Amazon is thought to own only a single-digit % stake in Anthropic, and has no seat on its governing board.

Thus, the MSFT–OpenAI collaboration rests more on equity ties, visible in MSFT’s revenue share from OpenAI and its prior exclusive API distribution rights. Amazon, by contrast, benefits via commercial terms with Anthropic, not revenue-sharing.

b. Open collaboration, deep technical lock-in: Amazon and Anthropic are primarily bound by technology. Anthropic trains heavily on Trainium-class chips, and its core code is co-optimized with Amazon’s ASICs and toolchains.

Anthropic cannot ‘lift-and-shift’ away without pain; migration costs would be high. Meanwhile Microsoft’s in-house chip efforts lag, GPT leans on Nvidia’s ecosystem, so OpenAI depends less on MSFT specifically.

3) What Project Rainier tells us

Project Rainier is a mega-scale compute campus built on Amazon’s Trainium to meet Anthropic’s training/inference needs. Two campuses have been announced — New Carlisle and Northern Indiana — with disclosed plans as follows.

a. New Carlisle is the first site, with $11 bn planned capex and 2.2–2.3 GW capacity. Construction began in Sept 2024, with first production in late Oct 2025 (about 500k Trn2 in the initial wave). Per Wells Fargo, Phase 1 reached full availability in early 2026 at ~1.3 GW, implying ~1.7 mn Trn2.

With Trainium 3 mass production planned for mid-2026, Rainier will deploy both Trn2 and Trn3. Another ~0.9–1.0 GW is expected to be added, much of it likely completed during 2026.

b. Northern Indiana was announced in late 2025 with ~2.4 GW capacity and $15 bn planned capex. Public details are limited, but reports indicate construction kicked off in May.

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From these data points, several takeaways stand out. First, the combined announced Rainier capacity is ~4.6–4.7 GW, largely Trainium-based, aligning closely with Anthropic’s Apr commitment to use 5 GW of Trainium.

Thus, Rainier’s build cadence is a barometer for the Amazon–Anthropic relationship. Progress here is a leading signal.

Second, Amazon appears to need ~15–16 months to go from zero to a 1+ GW campus, roughly in line with Oracle’s pace (Abilene Phase 2 at ~1 GW in ~15 months).

Third, based on disclosed capex, Rainier’s unit capex is ~$5–6 bn per GW, far below Nvidia’s oft-cited $50 bn per GW framework. The scope of the $11 bn and $15 bn is unclear — whether it covers just datacenter shells and base infra vs. chips/servers too.

So we cannot simply conclude Trainium’s all-in per-GW build cost is 1/10th of Nvidia GPU stacks. But it is reasonable to infer Trainium’s per-GW all-in build cost is materially lower than Nvidia-based systems.

Fourth, Anthropic’s $100 bn/10-yr spend for 5 GW implies per-GW annual revenue well below the ~$10 bn/GW industry heuristic. One external estimate (as of late 2025) pegs New Carlisle’s 2.2 GW at ~$14 bn/year revenue after discounts, implying ~60–65% of the industry’s per-GW revenue norm.

Together, these suggest Trainium lowers build cost for operators and usage cost for customers vs. Nvidia-based stacks. That said, Trn2 and Trn3’s absolute performance trails — Trn2 is ~60% of H200, Trn3 only slightly above. Lower pricing is therefore logical.

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III. How strong are Amazon’s ASICs.

Compute, chips, and models are the three pillars of AI capability. Amazon’s in-house chips are key to binding Anthropic and establishing a cost edge in cloud, so we review its chip roadmap.

2.1 Amazon’s in-house chip lines and timeline

Amazon’s chip story began with the 2015 acquisition of Annapurna Labs. It now runs four parallel tracks — Nitro (control/storage), Graviton (ARM-based general-purpose CPU), Inferentia (inference ASIC), and Trainium (training & inference ASIC) as follows.

1) Nitro / Nitro SSD: Amazon’s first hardware line launched in 2017. Nitro is not customer-rented compute but dedicated control-plane hardware handling virtualization, network, storage, scheduling, and security to offload overhead and improve efficiency, thereby lowering AWS cost structure.

2) Graviton: ARM-based general CPUs, with Gen 1 in 2018. Early gens competed on lower price/watt, handling lighter workloads cost-effectively.

After multiple iterations, Gen 5 (launched late 2025, mass-scale in 2026) is no longer far off mainstream x86 in the same era. Graviton is now widely available to customers and is likely the most deployed Amazon chip today.

3) Inferentia: Initially a dedicated inference ASIC for traditional ML, announced in late 2018, powering search ranking, personalization, and image/speech tasks.

It later targeted LLM inference, but rising inference demands and Trainium’s ability to handle inference mean Inferentia has been partially displaced. Gen 2 launched in late 2022, and there has been no new release since.

4) Trainium: Amazon’s key line in the LLM era. Gen 1 was announced in 2020 (deployed in 2022), originally focused on training traditional ML.

With GenAI becoming mainstream, three generations in five years (Gen 4 in development) have evolved into a training-and-inference workhorse against Nvidia GPUs and Google TPUs.

In short, Nitro is internal, and Inferentia’s role has narrowed. The focus areas are Trainium and Graviton, which we assess next.

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2.2 Performance comparisons

The strategic rationale is to reduce dependency on external hardware and drive better perf/watt and perf/$ via hardware–software co-design, ultimately expanding cloud margins. The best comparison is system-level, not chip-only, so we reference AWS instance data.

1) Graviton delivers strong value

On paper, the table compares Graviton generations with rival CPUs in equivalent AWS instances. Max CPU count indicates parallelism, network bandwidth captures external I/O, and EBS bandwidth reflects storage I/O.

Using ‘M’ (general-purpose) instances at max CPU count, Graviton 5 instance specs are broadly comparable to the latest Intel Xeon 6 and AMD EPYC 5th-Gen already in market. However, Graviton 5 hit GA later (late 2025/2026), so it is roughly one generation behind AMD/Intel in cadence.

Notably, Graviton instances often have stronger I/O. For I/O-optimized variants, Graviton 4 can hit 600 Gbps network and 300 Gbps EBS, while comparable EPYC Gen 4 tops out at 300/50 Gbps (Intel is lower).

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On realized performance, while Graviton 5 lacks broad public benchmarks, OpenBenchmarking shows Graviton 4 vs. AMD EPYC 9R14 and Intel Xeon 8488 across common tests. Graviton’s avg. score is similar to Xeon 8488C and ~80% of EPYC 9R14.

At then-current instance pricing (late 2024), perf per $ put Graviton 4 first, ~3.4% above EPYC and ~18% above Xeon. This indicates that despite lower absolute peak vs. AMD’s best, Graviton delivers superior ROI vs. prior-gen flagships in AWS, appealing to value-focused workloads.

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2) Trainium 4’s potential

Trainium is offered mainly as large UltraServers/clusters for big customers, so public benchmarks, especially for Gen 3/4, are sparse. We rely on disclosed specs and targets.

Single-chip capability indicates: Trn3 theoretical throughput (compute rate and memory bandwidth) just edges past Nvidia H200 (launched late 2023), but is well below Google TPU v7 (mid-2025). On FP8, Trn3 is roughly 55% of TPU v7.

Trn2, now widely deployed, delivers only ~50–60% of Trn3 on paper, so it is not yet truly competitive for flagship training. It fits inference or smaller-scale training better.

By contrast, Amazon’s stated targets for Trainium 4 would surpass TPU v8 and Nvidia B300, trailing only Rubin-based R100 in FP4. If achieved, Trn4 would leap to a leadership class, potentially winning flagship training/inference from top labs and accelerating AWS growth.

To be clear, these are targets on paper. Trn4 has no firm tape-out date yet.

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IV. In-house models: early days, large gap to close

Finally, we review Amazon’s weakest pillar — models. Amazon has launched the Nova family, but it trails SOTA by 1–2 major versions. Key observations follow.

a. Late start, slower cadence: Nova Gen 1 arrived in Dec 2024, with Nova 2 only by late 2025. Amazon’s model effort started late and iterates more slowly.

b. Multimodal track: Nova’s strategy favors breadth over specialization. Beyond the main line, Omni handles text, image, and video, while Sonic supports speech, reflecting a multi-version, multimodal approach.

c. Weaker absolute performance, faster response: Based on Amazon’s MMLU-Pro and other metrics, Nova 2 Lite is roughly on par with Gemini 2.5 Flash or Haiku 4.5, while Nova 2 Pro trades blows with Gemini 2.5 Pro or Sonnet 4.5.

So Nova 2’s top model is only comparable to the prior-gen mid-tier from leading labs. That said, it performs better on OCR and RealKIE for image and structured document recognition.

Amazon also emphasizes Nova’s responsiveness — faster time-to-first-token and higher tokens/sec. Nova 2 Lite and Pro both outpace peers on these latency metrics.

d. Given Nova’s current multimodal stance and capability, Nova is best suited to internal enterprise productivity — rapid processing of simpler, repetitive tasks like contracts/invoices, e-comm image search, and AI customer support.

In other words, Nova likely contributes more to cost leverage than to revenue growth for now. The commercial pull will come more from third-party SOTA on Bedrock.

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Summary: Amazon shows signs of moving from laggard back to leader in cloud AI. Its lead in MaaS is the standout, and its chip lineup is comprehensive, though still behind Google TPU on current-gen specs and closing fast.

On models, the gap remains, but tight collaboration with Anthropic is a viable bridge in the medium term. Amazon’s AI stack is far from weak overall and is improving.

longbridge Dolphin Research will continue to map other clouds’ AI strategies and capabilities, and will ultimately offer an industry view with stock preferences. Stay tuned.

<End>

Zhipu’s Jun outshone a persistently weak tape. First, the pre-Jun inclusion news (index eligibility) sparked sharp flows on the day. Then from Jun 10 to Jun 18, the stock doubled in roughly a week, and on Jun 22, $KNOWLEDGE ATLAS.HK broke the HK$1 tn market-cap mark.Such strength beat the market’s expectations. 

In our prior earnings take, we noted Zhipu’s greater momentum vs. $MINIMAX-W.HK, with the core view that capital is pricing the scarcity of model intelligence. At today’s valuation, that remains a reasonable lens, but ‘intelligence scarcity’ alone is clearly not sufficient to explain the move.

1) Is ARR the driver?

Zhipu’s valuation framework had already shifted post-CNY rally toward an overseas B2B comp set (Claude/Anthropic). Looking at Anthropic’s ARR curve, there is no clear slowdown yet; YTD it even accelerated, with Apr/May monthly growth over 55%. This has expanded the market’s imagination for Zhipu’s growth path.

Source: Dolphin Research

In our last note, we offered a reference: assume Zhipu replicates Anthropic’s cadence and takes ~1 year (implying ~12% MoM) to reach $1 bn ARR; at Anthropic’s P/ARR multiple, implied EV would be ~$60 bn. But Zhipu’s market cap is already ~$150 bn. Back-solving with the same Anthropic P/ARR suggests implied ARR of ~$4 bn and a Mar–Jun MoM of ~150%, which looks unrealistic.

Souce: Public Info, Anthropic, Dolphin Research

Taking a step back and using Anthropic’s scale-up phase as a check: Anthropic went from $1 bn to $5 bn ARR in ~half a year, running ~30% MoM. 

If we assume Zhipu is already at ~$1 bn ARR now (which itself implies ~60% MoM over the past three months) and then grows another six months at ~30% MoM, ARR would reach ~$5 bn. On Anthropic’s multiple, that roughly squares with a ~$150 bn market cap.In other words, today’s valuation embeds at least two layers of expectations: (1) Zhipu’s ARR is already near ~$1 bn, and (2) MoM of ~30% can be sustained for the next half year. Yet its most recent annual report disclosed official ARR of only ~$250 mn. That implies ARR rising from ~$250 mn to ~$1 bn in three months and then holding ~30% MoM for six more months, a very demanding setup.

Source: Dolphin Research

While current ARR specifics remain unclear, the observable volume-price dynamics suggest Zhipu is in a phase of rising volumes and rising price. Hence, a rapid ARR uptrend looks well anchored.

Volume: On OpenRouter, overall platform model calls continue to rise. For Zhipu, token calls on OR grew ~40% MoM on Avg. from Jan–Jun 2026 (Longbridge Dolphin Research Est.), but only ~8% since Mar, partly because usage spikes are highly synchronized with new model releases, which typically drive about a month of intense calling before normalizing.

Source: OpenRouter, Dolphin Research *The Timepoint means the start of a week

Price: On headline API cards, there appears to be no hike (5.2 vs. 5.1). In practice, GLM-5.2 shifted from tiered pricing to a blended rate, moving volumes that previously enjoyed lower tiers to the higher rate, effectively a small hike. Relative to peers, Zhipu’s pricing is now near the upper bound among domestic models.

We see two reasons why premium pricing holds: scarcity of intelligence underpins pricing power, and a primarily enterprise-facing mix means B-end clients focus on productivity gains from higher-intelligence models and are less price-sensitive.

Source: Z.ai, Dolphin Research

Comparing two biz. models, C-end exemplified by MiniMax/OpenAI vs. B-end by Anthropic/Zhipu, the latter has outperformed on valuation, with the B-end monetization narrative now largely proven.

Anthropic has paired stronger intelligence with the highest pricing; once it hit the intelligence ceiling, users complained yet still paid, and Zhipu’s price hike on its Coding Plan in Feb was read as confidence in its model strength.

By contrast, on the C-end, attempts like Doubao’s paid plan or OpenAI’s ads risk user backlash. Netting the volume/price analysis above, usage is indeed growing fast (though clearly below our earlier back-solve), and price has inched up implicitly, yet this still struggles to underwrite the core assumption that Zhipu is already near $1 bn ARR. Thus, the sharp rally looks more like multiple expansion.

2) Where does the multiple expansion come from?

1) GLM-5.2 is the first domestic model to crack the global top-3 intelligence ranks. On Jun 13, via its Coding Plan, Zhipu released GLM-5.2 and opened the API on Jun 17. GLM-5.2 is a 744 bn-parameter MoE model with 40 bn active params and a 1 mn-token context window, showing strong capabilities in coding and long-horizon agent workflows, with a sizable step-up vs. the prior gen, and it open-sourced weights under the MIT license.With GLM-5.2, Zhipu briefly ranked No.3 globally and No.1 in China on Artificial Analysis’s intelligence index, behind only Anthropic and OpenAI; its coding/agent scores were No.4/No.2 globally and No.1/No.1 in China (global ranks already edged down by Jun 22). On Arena.ai, GLM-5.2’s coding ranked No.2 globally, ahead of Opus 4.8 and behind only Fable 5. As open models narrow the gap with closed ones, founder Tang Jie publicly said Zhipu could surpass Anthropic within the year; given domestic pricing far below Anthropic’s (Zhipu API ~1/4 of Opus, ~1/10 of Fable 5), this materially boosted confidence in import substitution.

Source: Arena.ai, Dolphin Research

2) The U.S. shuts; China opens. As Washington ordered top U.S. models to pause services overseas and Anthropic cited export controls to suspend Fable 5, Zhipu almost simultaneously released an open-source version.

The contrast writes itself: while the U.S. tightens access to frontier tech, China in the same week released MIT-licensed, region-unrestricted weights.This contrast can lift multiples near term, but commercially GLM-5.2 does not directly benefit from the ban. First, Fable 5’s curbs are not permanent and reportedly are already being re-opened in stages.

Second, with a short window and a market view that GLM-5.2 still trails Opus 4.8 in overall UX, this demand is unlikely to translate into meaningful revenue upside.

Over a longer horizon, if Zhipu delivers a model on par with or surpassing Anthropic (notably, Zhipu was absent from Anthropic’s Feb list alleging model distillation by Chinese vendors), then coupled with recent moves by the U.S. and Anthropic to tighten access, frontier models may be viewed as strategic assets in a geopolitical contest. The U.S. has two champions, OpenAI and Anthropic, while China currently has only Zhipu, making a valuation premium reasonable.

3) Ultra-thin free float. The truly free float is very low (on day 1, under 3% of total shares), and inclusion-driven passive demand compounded scarcity in the short term. As various lock-ups roll off in 2H, float should expand, so today’s scarcity is unlikely to be the norm.

Putting the three factors above together, a premium multiple is justified. But against the ARR framework in Section 1, even in an optimistic case where ARR tops $1 bn by Sep (implying ~30% MoM), and benchmarking Anthropic’s Avg. MoM of ~20% from $1 bn to $9 bn, Zhipu’s ARR would be ~$1.73 bn by end-2026. The implied P/ARR would still be ~80x, nearly 2x Anthropic’s contemporaneous multiple even under bullish assumptions.

 

<End of text>

On Jan 2, 2026, $BIREN TECH.HK debuted on HKEX at the top-end issue price of HKD 19.60. The stock opened +82.14%, with intraday mkt cap briefly topping HKD 100 bn and closing around HKD 82.5 bn. The retail tranche was oversubscribed by 2,347x, underscoring frenzied demand.

The rally reflects its scarcity as the first GPU listing in Hong Kong, and the 'China’s $NVIDIA.US' label supercharged capital interest. Stripping away the halo, the key question is whether Biren is a high-quality company.

1. Journey: A company forged under the Entity List

To understand Biren, you have to trace its path, which mirrors the trials of China’s domestic chip industry. The journey itself reads like a tough history of local semis.

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1) 2019–2022: VC backs domestic chip substitution. The company was founded in 2019, the year the U.S. put Huawei on the Entity List and HiSilicon was forced to stop supplies. Primary markets pivoted to the 'domestic chip substitution' theme, with China’s GPU gap demanding startups that could carry the 'domestic NVIDIA' narrative while protecting investors’ capital.

Against that backdrop, founder Zhang Wen stepped in with a highly eclectic resume. He studied engineering at SJTU and later law and biz overseas, started as a lawyer, then turned investor, and only entered semis in 2011, assembling a top-tier GPU team from AMD, Qualcomm and Huawei HiSilicon.

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With narrative, talent, vision and scarcity (neither $Moore Threads.SH nor $MetaX.SH existed then), capital flocked in. From 2019–2021, the company completed seven financing rounds totaling RMB 4.7 bn, with post-money valuation over RMB 11 bn at Series B.

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2) 2022–2024: From splashy product debut to forced line 'upgrades'. In Aug 2022, Biren unveiled BR100/104, with BR100 as the flagship. The company pitched it as '3x+ A100 compute and near the unreleased H100' and rolled out the self-developed BIRENSUPA stack, fueling a China H100 narrative.

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But the specs told a story: $Taiwan Semiconductor.US 7nm, CoWoS-S and HBM2e. Savvy investors could anticipate the next shoe to drop, and two months later BIS introduced 3A090 export controls, directly capturing BR100 parameters.

Four months on, in Jan 2023, Biren abruptly mass-produced BR106. Where did it come from? Longbridge dolphin Research believes BR106 was a 'de-rated' iteration of BR104 to navigate the new rules, likely still made at TSMC initially, then shifted onshore after it was added to the Entity List.

Note: In Oct 2023 Biren was targeted for blocking, as BIS added it and 12 other Chinese entities to the Entity List with footnote 4, requiring foundries to obtain licenses before deliveries. TSMC foundry access was shut.

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From sanctions to domestic line ramp, Biren moved reasonably fast. Yet across the two rounds of sanctions, co-founders Jiao Guofang and Xu Lingjie left, and with a high-valuation 'domestic NVIDIA' that suffered a flagship abort, spec downgrades, forced line shifts, and team departures, the market halo faded.

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3) 2024–present: Commercialization starts

The company regained cadence only in 2024. BR166, a dual-die high-compute config within the 100-series and currently the revenue driver, only entered mass production in Aug 2025.

There are three products under BR100: single-die BR106, dual-die BR166 (revenue driver) and IoT chip BR110. In essence, all three trace back to designs from 2022.

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Including Biren, the value reset for domestic chip startups came in H1 2025 when H20 was banned. Even after subsequent relaxations and H200 flows, Chinese cloud budgets were rigidly allocated to domestic chips, cementing 'national chips' as a must-have under substitution.

With the new narrative in force, Moore Threads and Muxi listed on STAR, while Biren and $ILUVATAR COREX.HK listed in Hong Kong, and Enflame is set to follow soon. Within this repricing, what makes Biren different, how long can it ride the tailwinds, and where are the core watchpoints? We continue.

2. Near- to mid-term: Shipments rule

For domestic chips, the near-term earnings logic is simple: supply rules. Whoever can ship at scale captures cloud capex budgets, as $NVIDIA.US went from near-100% share in China’s GPU market to almost zero, and the Agent era further exploded demand. The supply-demand gap is massive.

By Longbridge dolphin Research’s rough math, the gap likely won’t close until 2028–2029. Demand becomes the main driver only after 2029, and the 2026 shortfall should be around 40–50%.

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1) Supply: Advanced capacity is scarce

It’s consensus that domestic GPU single-die performance lags, typically 1–2 generations behind NVIDIA/$AMD.US. Local players stack dies (Die-Die/GPU-GPU) and interconnect to bridge gaps, but they can’t erase process disadvantages vs. offshore foundries.

- EDA: Local EDA leader $EMPYREAN.SZ holds ~15.7% share domestically, but only 2–3% globally. It has yet to offer a full digital IC suite, and the U.S. tightly controls exports of EDA for advanced nodes (≤7nm).

- Lithography: China still relies on $ASML.US DUV imports. Procurement accelerated in 2025 to hedge tighter controls, while EUV export bans remain the critical choke point.

- Foundry: Equipment limits mean domestic advanced nodes (N+2: 7nm; N+3: 5nm) are scarce capacity. $SMIC.HK holds most of the domestic advanced capability, with many local GPU designers queuing for SMIC capacity. Hua Hong Semi offers small-scale production, but yield and capacity need validation.

Given capacity constraints, some GPU vendors have to use N+1 lines (first-gen 7nm). Performance is tightly tied to the process, so this is an unavoidable trade-off under scarcity.

Longbridge dolphin Research estimates supply via 'wafer capacity → AI allocation → manufacturing yield → dies per wafer → packaging yield → total die supply'. It’s a rough framework to size deliveries.

2) Demand: AI agent demand is surging

On demand, imports of AI logic chips are materially constrained, making domestic substitution a necessity. As most local products still struggle for training, we focus mainly on inference demand for now.

a) Internet majors such as Alibaba, ByteDance and Tencent.

b) Foundation model players like DeepSeek, MiniMax and Zhipu. They consume tokens non-stop, have outsized single-point demand, and can bypass CSPs to request directly.

These cohorts benefit from structurally higher inference loads. Leading models are already consuming over 10 tn tokens per day, making them the primary demand drivers.

c) Telcos, SOEs and local governments as sovereign AI players. Demand stems from national AI infra, data sovereignty, compute centers and public sector applications.

We derive demand via domestic AI capex plans, chaining 'client AI/cloud capex → minus CSP intl spend → ×server mix ×AI server mix ×accelerator mix → AI GPU TAM → /ASP/compute to back-solve cards → GPU die count → CPU:GPU ratio → CPU die count → total die demand'.

Overall, 2026 requires ~4.2 mn AI chips, while supply is only ~2.6 mn. The gap is visible to the naked eye.

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3. Short term: How strong is Biren’s capacity-locking?

To gauge Biren’s near-term revenue burst potential, the essence is whether it can lock enough capacity to ship at scale. Our checks indicate foundry capacity is rationed via a quota-like system in today’s tight environment.

Capacity is tilted toward firms with growth potential, avoiding extreme concentration at a single head provider. Under this planned allocation, Longbridge dolphin Research believes Biren, as a relatively second-tier chip vendor (we explain why below), may actually benefit.

1) Baseline to get quota: Pass model readiness

Quota allocation prioritizes Day 0 support the moment models release, with a strong emphasis on software capability. Domestic AI chip firms work closely with local model developers to achieve Day 0/deep adaptation.

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Most leading vendors claim Day 0 support at release. Actual runtime smoothness is another matter, but Biren’s compatibility with mainstream models meets the pass baseline.

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Next, the 'soft' condition for locking capacity: funding and relationships. Sovereign capital matters.

- Sovereign: Hygon/Cambricon have strong central state backing, likely to benefit first from SMIC’s advanced lines (Huawei still top priority). Shanghai also supports Biren/Muxi/Enflame locally, so Biren is not the sole favorite.

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- Industry: Before IPO, Enflame/Cambricon had Tencent/Alibaba on board, giving stronger demand-side assurance vs. Biren. Biren currently lacks a tightly bound mega-client relationship.

Despite no unique 'deep ties', it should still secure quota. For local gov., all vendors are stakeholders and hard to favor one over another, which helps second-tier firms get better-than-market capacity shares.

Second, $HUA HONG GRACE.HK (Shanghai SASAC 51.59%, Shanghai Guosheng 18.36%, Shanghai Intl Group 18.36%, Shanghai Instrument 11.69%) appeared as a 'close associate of existing minority shareholders' in Biren’s placement list (albeit at just 0.13%). In short, a wafer foundry is a shareholder.

Our checks also show Biren is engaging foundries beyond SMIC and has prepared two product paths. As Hua Hong’s 7nm ramps, Biren should gradually secure wafer supply alongside Hua Hong’s capacity release.

2) Why might yield constrain?

Even with the above positives, SMIC’s advanced capacity still won’t prioritize Biren. We expect most supply to come from Hua Hong, whose advanced nodes are early in ramp, with capacity to climb over time.

These nodes rely on DUV multi-patterning rather than EUV. That typically means lower yields and energy efficiency, which constrain shipments and form the most critical near-term risk.

4. Long term: Can Biren outperform?

Over the long run, competition is about product strength. It spans three dimensions.

a. Single-die capability: core specs and iteration speed. b. Hardware-software integration and ecosystem. c. System-level delivery.

Among domestic players, only HiSilicon has strong system-level delivery so far. Most are still focused on the chip alone, and for Biren specifically: 1) GPGPU-based hardware systems; 2) BIRENSUPA software platform.

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1) GPGPU-based hardware systems

In AI accelerators, ASIC, FPGA and GPGPU are the mainstream tracks. Biren designs GPGPU chips, accelerator cards and servers, operating fabless and outsourcing wafer manufacturing and packaging/test.

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Biren has mass-produced BR106/BR110/BR166, delivered via PCIe cards and OAM modules. The core specs have been summarized as follows.

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After BR100/104 were aborted, the company stopped disclosing fully comparable specs. Everbright Securities notes single-die (BR100/104/106) performance is strong among domestic peers, supporting a +273% YoY surge in total chip shipments.

Those are historical products, while Longbridge dolphin Research focuses on the BR166 flagship and the coming BR20X series. They matter more for forward competitiveness.

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a) Is BR166 competitive?

BR166 is essentially a dual-die BR106 with doubled compute and unchanged architecture, and BR106 traces back to the 2022 BR100 architecture. Per the prospectus: 'we use chiplet tech to integrate two BR106 dies and four DRAMs in one package… D2D bidirectional bandwidth between the two BR106 dies reaches up to 896 GB/s.'

The architecture (diagram below shows BR100; BR166 follows it with DRAM memory disclosure) was innovative for AI workloads then. It paired high compute with a very large on-die L2 cache (H100 50 MB vs. BR100 256 MB).

At the micro-architecture level, NVIDIA’s SM is the base scheduling unit, while Biren’s SPC breaks down into smaller EUs. It supports dynamic grouping across 4/8/16 EUs for finer-grained resource reuse based on workloads.

On matrix throughput, each EU has one T-Core (one SPC has 16 parallel T-Cores). NVIDIA’s Hopper uses four Tensor Cores per SM, and Biren’s approach suits chiplet architectures under domestic yield realities.

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Lack of native FP8 & FP4 support is the biggest flaw. Without low-precision formats, efficiency in large-model training and inference is structurally disadvantaged, and these formats are now widely adopted.

Among local peers, since BR100 dates back to 2022, single-die compute remains decent, but Biren has been outpaced in interconnect, memory bandwidth/capacity and precision. Ascend 910C essentially matches H100, and Muxi/Enflame products sit between H100–H200, leaving BR166 less compelling.

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Downstream partner mapping (yellow means delivered) corroborates this. Biren mainly ships to telcos/intelligent compute centers and sovereign AI projects, without large CSP orders.

A major reason is CSPs’ inference needs in the Agent era, which demand big memory and strong interconnect. Biren’s current lineup is relatively weak there, making BR166 a less competitive all-rounder.

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Beyond product strength, Biren’s origin story underscores geopolitical supply-chain uncertainty. That makes it hard to convince internet customers of long-term, stable, high-volume supply capability.

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That said, with demand running hot, those issues are less acute near term. Financials show visibility via orders, prepayments and inventory build.

- Backlog: Binding orders under framework and sales contracts total RMB 822 mn, supporting future revenue. - Contract liabilities: As of end-2025, contract liabilities were RMB 77 mn, indicating prepayments that lock orders.

- Inventory: End-2025 inventory net was RMB 949 mn, up over 500% YoY, with raw materials at RMB 386 mn and WIP at RMB 431 mn totaling ~85%. That suggests inventory expansion is driven by confirmed orders.

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b) What about BR20X?

BR166’s overall competitiveness is not standout, but at the 2026 juncture a 3–4 year major iteration is due. The new BR20X flagship is imminent.

(1) BR20X series is in physical design and tape-out validation, with commercial launch planned for Q4 2026. Biren will boost single-die capability and accelerate ultra-node systems.

(2) As the next-gen flagship fully on domestic supply chains and free of export controls, the evolution aligns with our expectations. - FP8 & FP4 will be supported in BR20X to speed large-model training/inference.

- Compute will be stronger. - Memory will be larger and faster, interconnect bandwidth higher, and ultra-node systems designed for scale.

We estimate BR20X will benchmark NVIDIA’s H200. There is no public spec yet; Longbridge dolphin Research derives an estimate from peer benchmarks and checks.

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The new line mainly plugs memory capacity gaps, while interconnect likely still needs improvement. Overall, fixing those weaknesses should secure mega-client orders on product merit.

The key constraint remains yield ramp on new lines. That will determine volume and delivery reliability.

2) BIRENSUPA software platform

On software, CUDA is the wall no one can bypass. Its moat comes from first-mover accumulation, deep HW/SW binding for performance and compatibility, plus rich toolchains.

Local responses split into two paths. One is CUDA compatibility, like Moore Threads’ MUSA and Muxi’s MACA, which they sell as commercialization enablers, and the other is full-stack self-developed ecosystems like Huawei’s CANN and Cambricon’s Neuware.

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The former binds sovereign orders via years of operator accumulation, while the latter goes deep in recommendation systems and wins ByteDance and other CSPs. Biren initially aimed for a self-developed ecosystem too.

BIRENSUPA’s open-source depth and stack richness are clear shortfalls, and the ecosystem is early. Engineering friction exists, with IR edits or higher compile error rates in complex scenarios, and some comm libs need tuning for sync latency in 1,000-card clusters.

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To address gaps, Biren plans to invest 40% of IPO proceeds in the software ecosystem. It is adding comprehensive support across PyTorch, vLLM, SGLang and other mainstream frameworks to tap the CUDA-built installed base and lower customer migration costs.

Near term, pivoting from pure self-developed to compatibility makes sense. Biren’s scale, client mix and commercialization maturity can’t support a fully proprietary path, and customers ultimately judge solution price/performance, a HW/SW combination.

Longer term, compatibility itself won’t create a moat. Even under optimistic assumptions, Biren could be a better middleware than peers, but without differentiation in ease of use, stability and third-party ecosystem, it risks homogenized competition.

3) Ultra-nodes: Is there a turnkey plan?

Biren delivers via GPU clusters/ultra-nodes, but its value-add is mostly in the GPU. Interconnect, switching and rack-level integration are largely handled by partners such as Xizhi and ZTE.

Outside Huawei, most domestic players are early in interconnect self-development (often limited to card-level protocols). Switch chips (NVSwitch analogues) and rack system integration are typically joint efforts with upstream/downstream partners.

R&D today concentrates on compute chips and software stacks, leaving interconnect undifferentiated. Yet in AI, interconnect value is magnified, as scale-up/out/across determines model sizes per node.

We believe firms that can deliver 'turnkey' first will better build long-term ecosystem moats. Biren is not fundamentally different from other startups in this respect.

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5. Net-net, Biren’s differentiation lies mainly in single-die capability. But for inference chips, the focus is shifting away from single-die compute toward memory and interconnect at the module level.

It has no edge in system-level ultra-node delivery, which is still early across the board, and in critical software ecosystems it’s not among the strongest. The positives are the quota model and continued policy support, and listing in Hong Kong provides an official endorsement.