[Excellent read] Summarised Takeaways from a HSBC research report where they sat down with 27 companies to gain first hand insight on key industry/sector narratives. A very useful conviction builder.
Before we get into it, it's Probably obvious but worth mentioning that where you see a point written like:
* **Celesta Capital:** about 60% of US data-centre projects reportedly face organized protests or political challenges, which delays permitting, power and local acceptance.
This means that the takeaway or information came from their conversation with Celesta Capital.
Particularly interesting takeaways regarding the bottlenecks IMO, from insiders that clearly intimately understand the extent of the bottlenecks.
Note TWLO in particular, were vocal and transparent about he fact that AI monetisation and driving revenue is still at a pretty early stage.
# Regarding AI Demand:
**Inference and agents are going to be a major driver of a big expansion in demand.**
* DeepInfra expects spending to shift from training to inference.
* AI agents can consume tens of millions of tokens in task loops, especially for coding, which means far more usage than human chat.
* NVIDIA also expects inference to become the larger market eventually.
**Demand is moving beyond a small group of hyperscalers and is broadening into Ai clouds, enterprise and sovereign.**
* NVIDIA says growth is broadening into AI clouds, enterprise and sovereign customers.
* Its ACIE segment already gets more than half its revenue from AI clouds and "neo-clouds".
* Enterprise and sovereign adoption is at an earlier stage and could outgrow hyperscale demand.
**Enterprise and sovereign AI are more durable drivers of demand (note these are the 2 sectors that PENG are exposed to which is why PENg benefits from what is likely to be more durable demand, less attached to hyperscalers)**
* HPE sees these deployments as less exposed to hyperscaler capex cycles and less vulnerable to white-box commoditization.
* It says its Private Cloud AI delivers 30–60% cost savings versus public-cloud, token-based models.
**Company specific comments to do with demand:**
* **SoftBank:** dismissed AI-bubble concerns, saying demand for compute remains well above supply.
* **Monolithic Power Systems:** described AI and data-centre demand as healthy and increasingly diversified, with ASIC revenue likely already exceeding GPU revenue.
* **GlobalFoundries:** said silicon-photonics demand signals are "flashing green lights", with more than 40 active customer engagements and a target of more than $1bn in silicon-photonics revenue by 2028.
* **Intel:** believes its addressable market is now about 15x larger than it was historically, which supports sustained double-digit growth. - wow.
* **Cohu:** customer equipment utilization has reached 80%, the level at which customers have historically started buying more capacity.
**OPEN SOURCE MODELS:**
Before June's tour, US companies were mostly focused on frontier models. Since then, Chinese open-source models have reset industry expectations about compute pricing, and open models have become a key debate in the US. The two camps are expected to coexist:
* **Open models** are preferred for cost, flexibility and sovereignty.
* **Closed models** keep the edge where frontier performance, safety, workflow integration or enterprise-grade capability is critical.
**Main advantages of Open source models:**
**Cost:**
* DeepInfra runs Llama, Mistral, DeepSeek and GLM at about one-tenth the cost of leading closed models, with competitive quality.
* Pinterest says open models cost less than 8% per transaction compared with proprietary models of similar size.
* HPE combines open-weight inference with intelligent routing to cut costs by 30–60%.
**Flexibility and control:**
* Pinterest post-trains open models in-house and tests models side by side, including US models such as Gemma and models from Asia.
* DeepInfra offers both shared serverless hosting and dedicated private deployments.
* Open models support data sovereignty, which matters most for enterprise, government and regulated customers.
**Supply Bottlenecks:**
The bottlenecks fall into four areas: data-centre capacity and power; substrates, wafers and advanced packaging; optical components; and memory. Companies are responding by pre-booking capacity, diversifying suppliers, integrating vertically where they can and holding more inventory.
**Data-centre capacity and power - Confirms what we already knew that Power is a massive bottleneck.**
* **DeepInfra:** it can build an inference cluster in as little as 90 days, but suitable data-centre space is getting harder to secure. The main constraints are:
* limited space;
* tax incentives being removed in some US states;
* securing liquid-cooled clusters for next-generation chips.
* **Celesta Capital:** about 60% of US data-centre projects reportedly face organized protests or political challenges, which delays permitting, power and local acceptance. Celesta sees space-based data centres as a possible long-term alternative, but transport and deployment currently cost about 20x as much as terrestrial facilities.
* **NVIDIA:** many older data centres can't support the power and liquid-cooling needs of systems like Vera Rubin. Much current spending is therefore effectively greenfield, requiring new infrastructure rather than simple upgrades.
**Substrates, foundry wafers and advanced packaging**
* **NVIDIA:** demand is well above supply. Management called next year's 70% growth supply-constrained and said unconstrained demand is materially higher. Securing wafer allocation from TSMC is strategically critical.
* **Intel:** server demand is strong, but Intel can currently fulfil only about 50% of it. The constraints are:
* ABF substrate availability, which is more of a limit than wafer supply;
* yield challenges with large reticle sizes for EMIB-T packaging;
* supplier learning curves for advanced packaging substrates.
* **Qualcomm:** its $5bn FY27 data-centre revenue target is supply-constrained, not demand-constrained. It has good visibility for FY27 but still faces tight supply in advanced packaging, substrates, testers and memory stacking.
* **GlobalFoundries:** industry capacity is tight, especially for data-centre-linked processes.
* **Applied Materials:** manufacturing is getting more complex because of:
* increasing logic content in DRAM;
* advanced 300mm packaging;
* TSV and wafer-level packaging;
* tighter requirements for yield, power efficiency and variability control.
**Optical components - Bottlenecks are STRUCTURAL.**
* **Arista:** it is supply-constrained through at least 2027, especially in 3nm chips, transceivers and high-speed optics. If supply unlocks earlier than expected, it could beat its current 40% growth guidance.
* **Lumentum:** it described one of the clearest component-level bottlenecks, a severe shortage of 200G-lane continuous-wave lasers. Supply of six-inch laser substrates is also limited, with some sulphur-doped substrates reportedly yielding as low as 5%.
* **Lightmatter:** the bottlenecks are structural rather than simple shortages: Co-packaged optics (CPO) adoption has also been delayed, with timelines moving toward 2028–30.
* insufficient bandwidth per switch;
* the need for longer reach than one-metre copper cables provide;
* the "shoreline" limit of conventional chips, where I/O can only sit around the chip's edge.
* **Marvell:** bottlenecks run across the optical supply chain, particularly lasers, silicon photonics and assembly.
**Memory and storage**
* **Cohu:** demand for HBM inspection is growing because a failed die on a finished GPU board can cost about $25k. The bottlenecks are mostly operational:
* many customer qualifications running at once;
* limited engineering resources;
* the need to protect lead times and availability for existing customers;
* growing complexity in testing HBM3, HBM4 and eventually HBM5.
* **Seagate:** HAMR components have long production cycles. Heads take three to four quarters to make, which creates a lag between investment and revenue.
* **Western Digital:** NAND and DRAM components in its bill of materials are constrained. Inflation and input costs make its traditional 10% annual cost-reduction target harder to hit. It expects to pass some costs through and to use long-term agreements to keep pricing more predictable.
# Revenue conversion from the AI usage still needs a lot of work:
AI monetization is moving beyond experimentation, but revenue is still modest relative to usage and investment. Companies inside the workflow or communications layer look better placed than those building frontier models themselves.
* **Twilio** was the clearest voice of caution:
* AI voice and agent adoption is promising, but **monetization is early.**
* Customers often start with limited production pilots, especially in regulated industries.
* AI hasn't yet materially changed adoption or revenue mix, and the pace of conversion from pilot to scale is uncertain.
* **Pinterest** has strong engagement, but monetization has to catch up:
* it has 640m monthly active users and sustained user growth;
* international monetization lags peers;
* $50m of AI-related GPU investment is a margin headwind, though the business remains highly cash-generative.
* **Adobe:** AI-first ARR is about $650m, less than 2% of its roughly $28bn revenue base. It is moving from seat-based subscriptions to a hybrid model with generative credits, so revenue can rise with usage.
* **HPE:** it monetizes AI through integrated infrastructure and private-cloud deployments rather than token-based public-cloud consumption.
* **Cadence:** it is still in the "price discovery" phase for AI design tools. It has more than 20 customer engagements for agentic AI offerings, but monetisation isn't mature.
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