Dolphin Research

CATL 3Q26 First Take: Overall, CATL delivered a 'shocking' print. Revenue still grew 59% YoY, but attributable NP rose only 15%, well below expectations. Details follow.

a) Revenue remained strong. It still missed the Street.

In Q3 2026, revenue came in at RMB 165.2 bn (+59% YoY), below market consensus of RMB 175.0 bn. The shortfall vs. expectations was notable.

Growth remained shipment-driven, helped by the seasonal Q3 peak and customers pulling forward purchases ahead of the 2% battery consumption tax effective Sept 1. Average battery ASPs fell QoQ as lithium carbonate prices dropped rapidly since May, dragging on revenue.

b) GPM fell QoQ. In Q3 2026, blended GPM was 20.7% vs. the Street at 21.5%.

That was down ~250bps QoQ from 23.2%, marking a third straight quarterly decline. The trend remains unfavorable.

GP was approx. RMB 34.2 bn, roughly flat vs. Q2. Revenue rose nearly 12% QoQ, yet GP did not increase.

The QoQ GPM compression was likely driven by several factors. Battery prices were cut alongside lithium-linked pricing, and pricing on newly signed energy storage orders also trended down.

Export VAT rebates were reduced in stages and booked into costs. Inventory built at high prices in Q2, including raw materials and WIP, was consumed amid falling lithium prices, further depressing margins.

c) Net profit slid, and profit per Wh kept falling. Attributable NP was RMB 21.25 bn in Q3.

With revenue up 59% YoY, NP grew only 14.5% YoY and fell 6% QoQ. It even missed the lowered Street range of RMB 22–24 bn.

Dolphin Research sees the profit drag mainly from the following. See below.

① GPM declined QoQ. This was a key headwind.

② The 2% battery consumption tax effective in Sept: taxes and surcharges jumped from RMB 0.69 bn in Q2 to RMB 2.21 bn. The one-month increase was around RMB 1.53 bn, well above Dolphin Research's RMB 0.8 bn estimate.

③ R&D expense continued higher to RMB 7.14 bn (Q2: RMB 6.10 bn). Cost pressure intensified.

④ Impairment losses rose QoQ by RMB 1.2 bn to RMB 1.7 bn, given the sharp lithium price drop. This added to the earnings burden. $CATL(03750.HK) $CATL(300750.SZ)

AI workplace tools face a core monetization challenge: chatbots can scale to massive user bases, yet paid conversion stays low. By contrast, coding has emerged as the first breakout use case. What explains the gap?

It comes down to whether the AI’s value is sellable: (1) verifiable efficacy; (2) measurable revenue uplift or cost savings. Chatbots mostly deliver suggestions, making time or cost saved hard to quantify. Quality often feels subjective, and users must rerun the workflow to validate.

High validation costs make trust hard to build, so usage stays at shallow Q&A. Such low-feedback data does little for post-deployment tuning, forcing platforms to buy and label high-quality datasets. The less users trust, the fewer data assets accumulate, and switching costs stay low.

In short, chatbots can maximize user scale. But they struggle to lift paid conversion.

Coding ticks both boxes: the knowledge scope is bounded, and outputs are automatically verifiable. The incremental value maps directly to higher-paid IT talent. Data backs this up — within a year, AI coding adoption among IT staff topped 70%, with AI-assisted code near 30% of output, which is why it broke out earlier than chatbots. $Alibaba(BABA.US) $TENCENT-R(80700.HK)

Where will AI money actually come from? That is the core question now.The playbook of subsidize first, build habits, then harvest no longer works—at least for now—given compute costs. Consumer chatbots have low pay rates and short daily usage, making C-side monetization unusually hard.

So where is the real willingness to pay? The answer points to task-oriented Agents, which, once introduced, have unlocked enterprise demand.

A Stack Overflow sample survey shows 59% developer Agent usage, with 37% using them daily. Among developers using AI tools, 66% use coding agents and 73% of them use them daily.OpenAI’s revenue mix also tilts to enterprise: by Jul, B2B (API, enterprise subscriptions) had surpassed consumer (which was 40% in Apr), with ARR at $20bn+. It doubled by end-Sep.On a $65bn gross ARR basis (net $50bn+), B2B accounts for 60%+. Back-solving implies the consumer side at ~$25bn, up less than 40% vs. Jul.

Advertising isn’t smooth either: CPM fell from ~$60 in early gray tests to $15–25, and the self-serve minimum dropped from $50k to zero. By end-Aug, ad ARR reached $1bn.It spans tens of thousands of advertisers across 40+ countries and regions.

IBKR 3Q26 First Take: Q3 results slightly beat. Compared with SCHW’s print last night, IBKR’s less diversified biz looked more solid, in our view due to stronger competitiveness-driven organic growth.

Q3’s market backdrop was weak, and growth still hinged on rapid user expansion. Accounts reached 5.58 mn at end-Q3, with net adds of 0.39 mn QoQ; growth did slow, but the annualized pace remains around 30%.

By contrast, SCHW’s net account adds fell 35%, while IBKR’s decline was only about 10%. SCHW’s annualized growth was under 5%, underscoring IBKR’s product competitiveness.

Broader user penetration lifted total assets under custody (AUC) QoQ. This offset the drag from sharp Q3 market swings on client asset balances.

Earnings growth was driven by scale expansion and a steady NIM. Q3 NIM fell 40bps YoY, but interest-earning assets rose 28% YoY.Margin lending contributed the most, being both the largest and fastest-growing book.

Since mid-year, higher rate expectations in an inflationary setting have eased the YoY NIM compression headwind. The 25bps hike in Sep will flow through in Q4.In general, steady small hikes are marginally positive for IBKR.

However, wider divergence on AI investing and recurrent geopolitics are weakening risk appetite. This could erode IBKR’s rate tailwinds.

On costs, IBKR’s operating efficiency kept opex broadly stable. Notably, G&A (incl. marketing) rose 15% QoQ.While the absolute increase is modest at about $10 mn, the trend vs. prior years suggests IBKR is stepping up marketing to capitalize on current user migration tailwinds.

Beyond CAC, product innovation continued: the Agentic autonomous trading feature rolled out and improved in Jun–Jul, prediction markets expanded by connecting more platforms, and IBKR is gradually onboarding smaller RIAs. We therefore expect ongoing incremental marketing spend for product rollouts and promotion.

Even so, management’s discipline on expenses should keep profits relatively stable. OP reached $1.5 bn (+15%), with OPM at 78%, down slightly YoY.$Interactive Brokers(IBKR.US)

TSMC 3Q26 First Take: results were solid, but the market wants clarity on 2027 and beyond. With monthly ops data already disclosed, revenue came in at $46.7bn (+41% YoY), ahead of the $45.8bn consensus. On a TWD basis, revenue growth was 51% this quarter.

Versus the pre-announced revenue, gross margin was the key focus. GPM was 66.4%, down 130bps QoQ and slightly below the Street at 66.8%. The pullback reflects the early drag from the 2nm ramp.

The market expects 2027 ASP hikes: advanced nodes (≤7nm) up about 5–10%, and mature nodes (≥N7) low single digits. Combined with the 2nm ramp, GPM should resume an uptrend.

With fabs running full, the company has stronger incentive to lift capex. It maintained 2026 capex at $60–64bn, unchanged from last quarter. This implies next-quarter capex of $19.3–23.4bn vs. $11.5bn a year ago, up 67–103% YoY.

Investors are watching management’s forward capex outlook. The market now pencils in roughly $85bn for 2027, about +35% YoY.

For next quarter, the company guides revenue of $52.3–54.3bn (above the $51.0bn Street) and GPM of 65–67% (Street at 66.8%). It raised its 2026 revenue growth outlook again to 44%, from slightly above 40% last quarter.

Repeated revenue beats underscore solid operations. With 2026 largely in the bag, investors care more about the 2027 outlook. A clear guide for 40%+ full-year growth in 2027 would provide the confidence the market seeks.

With the AI supply chain under pressure, the market is looking to TSMC to deliver a sufficiently explicit growth guide to lift sentiment. For more, follow Dolphin Research’s forthcoming takeaways and Trans. $Taiwan Semiconductor(TSM.US)

$Amazon(AMZN.US) AWS is not trying to beat Nvidia, AMD, or Google. Much of the networking hardware is off-the-shelf, so there may be no need to pay a premium for custom parts. Is networking really cheaper?

At first glance on a per-card networking cost basis, AWS's approach works: the rack form factor mirrors GB300, yet per-card networking cost is lower. The savings are mostly in optics: Trn3 uses AECs to reach the top-of-rack (ToR) switch, and optics are only used beyond the ToR.

On an equal-compute basis, however, Trn3's networking is not particularly cost-efficient and lacks competitiveness.

Based on the chart, Trn3's networking costs about $1,800 per PFLOPS of FP8 compute. That is the highest among the six comparison setups.

$Amazon(AMZN.US) NVIDIA's GB300 houses 72 GPUs in a single cabinet with full all-to-all connectivity.

By contrast, AWS Trn3 is constrained by the limited port count on its off-the-shelf switch chips.

Each compute tray integrates four Trn3s, which must be split into four isolated 'vertical ladder' planes.

$Amazon(AMZN.US) $Alphabet(GOOGL.US) Google can reach nearly 10k accelerators with direct connect. Why didn’t AWS stay on that path?

The short answer: AWS’s 3D torus is a stitched-together design and lacks Google’s prerequisites for scaling. A 3D torus—chips arranged as a cube with only nearest-neighbor links—does not require switches.Trn2-Ultra daisy-chains four 16-accelerator servers into a 4×4×4 topology, with all links on copper. Google’s approach looks similar, but the wrap-around links use a different medium: Google uses fiber into OCS, whereas AWS stays on copper.

The immediate consequence is a hard cap at 64 accelerators. With OCS plus its software stack, Google can tile cubes flexibly to approach nearly 10k cards. Trn2 has no switching elements, the backplane copper is fixed, and AECs (with retimers for longer reach) have length limits, so you can’t span more racks.

Overall, Trn2’s all-copper, switchless design limits scale. With too many gaps to fill and pressure to deploy, AWS pivoted to a switched fabric.Trn3 adds switch silicon, taking a domain to 144 accelerators and preventing hop count from growing with scale.

ASML 3Q26 First Take: results were broadly in line, with revenue and GPM meeting prior guidance. Quarterly growth was driven by pull-ins from China customers and TSM, alongside rising demand for upgrade services.

Beyond the reported figures, investors focused on guidance: the company raised its 2026 full-year outlook to €44–45bn (vs. €43–45bn last quarter). It only lifted the floor, not the ceiling, offering limited upside surprise.

ASML typically provides next-year guidance in Q3, but it did not issue 2027 guidance this time. Watch the later earnings call for management’s view on 2027–2028; Dolphin Research will post updates promptly.

Despite the broader AI-led market correction, ASML’s pullback has been relatively modest. Downstream foundry capex is clearly rising; for example, Micron guided FY2027 capex to over $50bn (+70% YoY).

Beyond AI demand, High-NA EUV could usher in another product upgrade cycle. This would underpin sustained high growth for ASML.

Overall, if management cannot articulate a clear 2027 outlook, the market may be disappointed near term. That said, given core fabs’ capex plans, downstream demand for semi equipment remains strong, and the company can still deliver high growth.

$ASML(ASML.US) $ASML 2X Long ETF(ASMG.US) $Direxion Daily ASML Bull 2X ETF(ASMU.US)