The Innovation Investor

Invest in the most innovative companies. A veteran investment manager and researcher.

24年资深华尔街基金管理经验，20年对冲基金管理人。
投资与科技、创新领域

[The Innovation Investor @OceanusResearch]

@alanchen https://t.co/LfTJRiWTSE

[The Innovation Investor @OceanusResearch]

The $70B run rate of OpenAI is what Axios came up with,  and then widely circulated later.

The gap is about how the run rate is counted. Anthropic treats itself as the seller when a customer buys Claude through AWS or Google Cloud. It books the full customer bill as revenue and records the cloud provider’s cut as a sales and marketing cost.

OpenAI does the opposite on Azure. The Information reported that for sales through Microsoft it counts only its own cut, about 20%, and treats Microsoft as the primary seller. On direct revenue it reports the number before the separate 20% revenue share it pays Microsoft.

OpenAI’s own figure to investors was approaching $50 billion at the end of September, counted its way: Azure resale booked net, only OpenAI’s cut. Investors then added the estimated cloud-partner revenue so the total could be lined up with Anthropic, which books the full customer bill through AWS and Google and treats the platform fee as a cost. That add was about $20 billion, which is how the late-September reports got to roughly $70 billion.

And that is what Axios Media came up with later.  Nothing really hasn't changed. Market took a dive, investors only read the headline.

[The Innovation Investor @OceanusResearch]

The $70B run rate of OpenAI is what Axios came up with,  and then widely circulated later.

The gap is about how the run rate is counted. Anthropic treats itself as the seller when a customer buys Claude through AWS or Google Cloud. It books the full customer bill as revenue and records the cloud provider’s cut as a sales and marketing cost.

OpenAI does the opposite on Azure. The Information reported that for sales through Microsoft it counts only its own cut, about 20%, and treats Microsoft as the primary seller. On direct revenue it reports the number before the separate 20% revenue share it pays Microsoft.

OpenAI’s own figure to investors was approaching $50 billion at the end of September, counted its way: Azure resale booked net, only OpenAI’s cut. Investors then added the estimated cloud-partner revenue so the total could be lined up with Anthropic, which books the full customer bill through AWS and Google and treats the platform fee as a cost. That add was about $20 billion, which is how the late-September reports got to roughly $70 billion.

And that is what Axios Media came up with later.  Nothing really hasn't changed. Market took a dive, investors only read the headline.

[The Innovation Investor @OceanusResearch]

OpenAI’s own figure to investors was approaching $50 billion at the end of September, counted its way: Azure resale booked net, only OpenAI’s cut. Investors then added the estimated cloud-partner revenue so the total could be lined up with Anthropic, which books the full customer bill through AWS and Google and treats the platform fee as a cost. That add was about $20 billion, which is how the late-September reports got to roughly $70 billion.

[The Innovation Investor @OceanusResearch]

@TJ_Research OpenAI does the opposite on Azure. The Information reported that for sales through Microsoft it counts only its own cut, about 20%, and treats Microsoft as the primary seller. On direct revenue it reports the number before the separate 20% revenue share it pays Microsoft.

[The Innovation Investor @OceanusResearch]

@TJ_Research The gap is about how the run rate is counted. Anthropic treats itself as the seller when a customer buys Claude through AWS or Google Cloud. It books the full customer bill as revenue and records the cloud provider’s cut as a sales and marketing cost.

[The Innovation Investor @OceanusResearch]

@laotangmeigu 坏掉的⏰每天对两次

[The Innovation Investor @OceanusResearch]

Bond vol has been much larger than equity vol (MOVE/VIX = 7, versus 4~5 norm). Seems to me that either equity investors are too relaxed, or this round of bond meltdown is too much.

[The Innovation Investor @OceanusResearch]

@Kay2289123 我就在纽约打拼了30年，一样单调乏味。但这不重要，寻求内心的安稳才是最重要的。

[The Innovation Investor @OceanusResearch]

@TJ_Research 25 bps, crush inflation. This scenario is probably unlikely.

[The Innovation Investor @OceanusResearch]

@RYANHINGSHING @jadejade12312 没什么逻辑，都是短线投机客罢了。这我看到的太多了。

[The Innovation Investor @OceanusResearch]

@BobEUnlimited simply not possible.

[The Innovation Investor @OceanusResearch]

@TJ_Research SOX目前的加权P/E是多少？

[The Innovation Investor @OceanusResearch]

@TJ_Research You need to be cautious about the software thesis. For example, Salesforce is still predominantly as platform of system of records. If Anthropic is allow to access the data, I think Claude will be the man entry point, potentially further descrupt seats, etc.

[qinbafrank @qinbafrank]

https://t.co/U5gMBQfTBj

[美股大数据 @StockWe_Com]

单笔投资回报率最高达46%！大摩重磅报告拆解AI投资：市场全算错了

市场的焦虑，正在制造一个认知差

如果你最近盯着科技股看，应该能感受到市场弥漫着一种情绪——投资者正在质疑：科技巨头每年砸下几百上千亿美元搞AI，到底什么时候能赚回来？

这种担忧直接反映在了股价上。微软、谷歌、亚马逊，今年虽然没跌多少，但估值始终被压着，涨不动的感觉。

美股投资网认为，市场在害怕一件事：AI投资会不会变成一场没有回报的军备竞赛？

但摩根士丹利刚刚发布了的重磅报告，标题就很直接——《通往25-50%生成式AI投资回报率之路》。

核心结论就一句话：市场算错了账。

大摩的底层逻辑：市场只看到了成本，没看到利润结构

市场当前的痛点是什么？

美股投资网认为，AI资本支出狂飙 + 模型训练费用天价 → 投资者严重质疑砸下去的钱能不能回来。

但大摩的增量判断是：随着行业从“训练时代”进入“推理时代”，这笔账完全算得过来。

所谓“推理时代”，说白了就是AI模型不再只是用来训练和炫技，而是真正开始被大规模调用、产生实际收入的阶段。你每次用ChatGPT、Claude，背后就是一次推理调用，每一次调用都有定价，都能产生收入。

大摩通过自下而上的测算得出结论：生成式AI投资能带来25%-50%的增量ROIC。
这不是在画饼，他们拆了三套具体的商业模式来证明。

路径一：云厂商GPU租赁——最稳的“卖水人”生意

第一套模式最直接：云巨头自己建数据中心，买GPU，然后把算力租给别人。

大摩假设一个1吉瓦的数据中心，部署大约41万颗GB300 GPU，总GPU小时数约36亿小时，利用率75%，租金每小时8.5美元。基于这套假设，测算结果是：
增量EBIT利润率约70%，ROIC超过30%。
核心杠杆就两个：GPU每小时定价 + 产能利用率。

定价越高、卡跑得越满，利润就越厚。云巨头在这个生态里扮演的是“卖水人”角色——不管谁家的模型跑得好，都得租我的算力。微软、亚马逊、谷歌，是大摩在这个环节最看好的赢家。

路径二：自有算力的模型API——利润率最高的玩法

第二套模式是模型公司自己买卡、自己跑模型、然后把模型能力以API形式卖给开发者。
大摩的假设是：65%的算力用于推理，每颗GPU每秒生成2750个Token，每百万Token定价1.75美元。在这个基准下：

增量EBIT利润率超过70%，ROIC约46%。
这才是整个AI产业链里利润率最厚的一环——因为你同时掌握了硬件效率和模型定价权。谷歌的Gemini、Meta的LLaMA API、xAI的Grok，都属于这条路径。

但大摩也强调了一个关键变量：Token吞吐量（每秒生成的Token数）是生死线。 模型越大，单卡吞吐量越低，但如果芯片代际升级（比如GB300比GB200吞吐量大幅提升），单位成本就会被摊薄。

路径三：租用算力的模型API——中间商赚差价，依然有得赚

第三套模式是模型公司自己不买卡，而是从云厂商那里租算力，再对外提供API服务。
这套模式的利润被云厂商分走了一层，但仍然是一门赚钱的生意：
增量EBIT利润率约30%，ROIC约25%。
虽然利润率不如前两条路径，但核心启示是：即使你不是云巨头、没有自己的GPU集群，只要模型做得好，依然能获得正向回报。 这个结论很关键——它意味着AI投资不是只有“超级巨头”才能玩得转。

赢家地图：谁在这轮AI投资中最受益？

基于以上三种模式，大摩给出了清晰的赢家判断：
基础设施底座赢家：微软、亚马逊、谷歌。
算力紧缺的生态里，云巨头是稀缺资源的掌握者。谁有卡，谁就有定价权。这三家的GPU租赁业务，将贡献可扩展的高利润收入。
模型与应用赢家：谷歌、Meta。

美股投资网认为，能持续推出差异化且成本优化的模型，就能通过高利润的推理收入实现变现。Meta最近刚开放了LLaMA API，谷歌的Gemini也在持续迭代——谁能在“模型质量”和“推理成本”之间找到最优解，谁就能吃掉最大的利润份额。

三个关键变量，决定这个算盘能不能打响

大摩在报告里还特别提到了三个值得持续跟踪的因素，它们将决定这套测算是否真的能兑现。

第一，芯片代际更迭。 从GB200到GB300，硬件性能的提升会直接改变折旧成本和吞吐量预期。每一代新芯片的性价比曲线，都将重新定义所有商业模式的盈利空间。

第二，Token经济学的反比例关系。 模型越大，定价可能越高，但单卡吞吐量会下降。如何在“大模型溢价”和“吞吐量损失”之间找到平衡点，决定了利润率的天花板。软硬件协同优化带来的“Token效率”，才是真正的核心竞争力。

第三，训练与推理的算力博弈。 这是一个常被忽视的矛盾：保持技术领先需要持续烧钱训练模型——但训练本身是纯成本，不产生任何收入。巨头必须在“不赚钱的训练投入”和“高利润的推理产出”之间做好产能分配，这对管理层来说是一个非常现实的权衡。

这不是估值游戏的终结，而是一次修正

大摩这份报告的意义，不是给AI投资贴上一个“合理”的标签就完了。

它真正想说的是：市场之前对AI投资回报率的判断，依据的是“训练时代”的逻辑——只见成本，不见收入。而随着推理需求爆发、Token定价体系成型、芯片效率持续提升，AI的商业模式正在从资本黑洞变成现金流引擎。

当然，这一切的前提是——芯片确实在迭代、Token需求确实在增长、定价体系确实能维持。但至少从数字上看，这笔账是有可能算得过来的。

接下来市场的关注点，大概率会从“砸了多少钱”转向“每颗GPU赚了多少钱”。

到那时，大摩这份报告的真正价值才会显现：它提供了一套锚——一个用来判断谁在裸泳、谁在赚钱的坐标系。#美股 $NVDA $TSM $AVGO $AMZN $META $MRVL

[Tech Fund @techfund1]

Good expert call on Bloom Energy $BE with a former VP at Plug Power - pretty bullish

Hyperscalers did not evaluate Bloom against gas turbines and select Bloom. They selected turbines, discovered they could not get them, and Bloom was the alternative that checked enough boxes.

Gas turbines from Mitsubishi, GE Vernova, Siemens and Hitachi remain the incumbent workhorse, but his read is that if the order is not already placed, you are not energizing before 2030. Reciprocating engines sit in the same position: Caterpillar, Jenbacher, Generac, Wärtsilä, all effectively sold out. Transformers, switchgear and substation equipment carry 60 month lead times.

What Bloom offered was availability plus modularity. A claimed 90 day time to power on smaller blocks, which he believes is credible at modest scale and unlikely at large scale, plus a build-as-you-go capital profile. Turbines want a single large plant. Behind-the-meter deployment wants building blocks you can add to as long as you have secured the land and the gas tap.

> Why the Turbine OEMs Will Not Simply Close the Window

Turbine and engine OEMs are deliberately not expanding capacity. They suspect the order book is double and triple booked, and they fear being left with stranded factory capacity when projects fail to reach FID. His analogy is the semiconductor capacity cycle, where consecutive quarters of poor absorption caused structural damage.

Their posture, as he characterizes the consensus from trade shows and industry conversation: you cannot buy it from me, you cannot buy it from my competitor, you will wait.

If that discipline holds, Bloom's window is measured in years rather than quarters, which is materially longer than the market appears to assume. Bloom's product is closer to a solid state electrochemical device than a precision machined turbine, drawing on an entirely separate supply chain that can be ramped faster.

> Levelized Cost: A Premium, But Not a Prohibitive One

He built his own LCOE model rather than relying on published work, which he found rested on unexamined assumptions. His output:

Gas turbine: roughly 4.5 to 7 cents per kWh
Bloom: just over 7 cents unsubsidized, below that with federal incentives
Reciprocating gas engine: roughly 8 to 10 cents
Diesel: high teens to mid 20s

The critical observation is that this is not a 3x premium for speed. That pattern collapses the moment supply normalizes, because buyers drop the expensive option as soon as the cheap one is obtainable. A single digit cent premium does not collapse, because the hyperscaler business case still clears at that price.

The offset to Bloom's higher capital cost is efficiency: 60 to 65 percent, against roughly 55 percent for a gas turbine and roughly 45 percent for a reciprocating engine. Bring capex down and the LCOE gap narrows or inverts.

> Where Bloom Ranks Today

Asked to stack rank for a hyperscaler buyer, he puts Bloom third, behind turbines and engines, purely on track record rather than physics. His analogy: you know exactly what you get from a Caterpillar engine or a GE Vernova turbine the way a Toyota buyer knows what he is getting. No buyer has that reflex for a Bloom box yet.

The open questions the buying community has not resolved: real world availability, whether maintenance cadence matches or beats turbine schedules, and the roughly 10 year stack replacement cycle. On that last point he offers a mild positive read-across, noting that in the PEM industry stack rebuild intervals came in longer than originally modeled.

The path to second or first place requires two things running together: two to four years of collective industry uptime data, and capex reduction. Oracle, Nebius, Brookfield and AEP are the proof points that will settle it. On whether they will work, he says "the jury is still out," while noting early evidence reads favorably.

> Non-Combustion as an Unpriced Permitting Asset

The Bloom box does not combust natural gas. It runs an electrochemical reaction. The consequences stack up in a specific and useful way:

NOx, SOx and particulate emissions at or very near zero, leaving local air quality unaffected
Roughly 65 dBA at three feet, which he compares to a lawnmower at fifty feet, meaning nearby highway noise dominates
Zero net water consumption, with startup water recycled as steam
Materially easier local permitting

Each of those neutralizes a specific community objection, and the pushback is accelerating. New York State's one year moratorium is the marker he points to, alongside complaints in other jurisdictions about power draw, water use and air quality.

His honest caveat: to date these attributes have played essentially zero role in purchase decisions. Availability and cost drove everything, and he assumes very little of Bloom's performance so far reflects environmental considerations.

If pushback becomes electoral, and he says he is watching whether candidates start running on it, then zero emission on-site generation stops being a nice-to-have and becomes the only permittable option across large parts of the country. He expects this to bite first at the 20, 50 and 100 MW sites going into actual neighborhoods rather than at the West Texas mega-campuses.

> Market Share Trajectory

Data center demand forecasts he is working from run 40 to 60 GW per year. Bloom's share today sits in single digits. His trajectory:

Five years: 15 to 18 percent
Ten years: 25 to 28 percent
Upside case, if emissions constraints become binding in enough jurisdictions: 40 to 50 percent

The constraint that drives the upside case is geographic. Not everyone can replicate what Microsoft and Chevron are doing on the West Texas gas fields. Once data centers have to disperse into places that care about permitting, the zero emissions conversation becomes unavoidable.

> The Bear Case He Actually Respects

Execution, not demand. He flags this above everything else.

Bloom has roughly 1.5 GW deployed against a backlog he characterizes as roughly 20 GW. On Sridhar's own description of the factories, that a visitor will see build activity and factory expansion activity running simultaneously, the expert's reaction is blunt. To an industrial engineer, expanding while still trying to build is a very risky proposition. Doable, but it is the precise point at which fast-scaling companies break, and he notes this is the classic failure mode for startups that find themselves in this position.

Q1 was clean. The Q2 print, due around the 28th, is the next checkpoint on whether execution is holding.

The secondary risks are demand-side and none of Bloom's own making: hyperscale capex circularity, bubble risk, and whether community pushback genuinely slows the build or simply reroutes it to Texas.

> Scandium: Directionally Fair, Materially Overblown

On the short thesis that Bloom cannot secure enough scandium, he says the report has some points but overstates them. His rebuttal runs on three tracks.

Cost sensitivity. Scandium is a dopant in the zirconium ceramic electrolyte, used at very low concentration, valued because it tolerates the 800 to 900 degree operating temperature. Even if it were 2 percent of materials cost, which he considers extraordinarily high for a dopant, a doubling in price takes it to 4 percent. Bloom likely has the pricing power to pass that through, and a half point efficiency gain would offset it in LCOE terms. His conclusion: more price risk than supply risk over the next couple of years.

Supply structure. Scandium is almost never mined primarily. It sits in the tailings of titanium, cobalt, aluminum, iron and lithium operations and is generally left behind. The binding constraint is processing capability, not geological availability, and that processing capacity is being built with national security tailwinds behind it. Scandium-aluminum alloys matter for 3D printing, fighter aircraft skins and missiles, which places it squarely in the critical minerals policy agenda.

Company mitigations. Bloom has spent 20 years reducing scandium loading per gigawatt. He located a patent application substituting cerium and yttrium, both more available, and Bloom holds IP on recovering scandium from mine tailings. He reads Bloom's willingness to address the topic directly, rather than deflect, as evidence they take it seriously rather than evidence of vulnerability. Non-Chinese supply exists: he points to Sumitomo's Philippines cobalt operation, which publicly identifies Bloom as a customer. Bloom does not disclose suppliers, and the short report's supply map traces its merchants back toward China.

> The Competitive Set

FuelCell Energy. Molten carbonate rather than solid oxide, but functionally similar: high temperature, slow start, direct natural gas, suited to stationary baseload. Why they never scaled into this comes down to inertia and strategic drift. Their historical focus was a trigeneration box producing hydrogen, power and heat, deployed for applications like Toyota Mirai fueling at the Port of LA. When hyperscale demand arrived they had nothing to show. His read on the pivot: they saw the multiple Bloom trades at and asked why not us.

Ceres Power. UK based, probably second globally in solid oxide IP. Pure licensing model, which means most licensees stay invisible. The disclosed one is Weichai, moving from small C&I units up to hyperscale scale. He doubts Weichai exports into the US successfully but expects success in China.

Microturbines and aeroderivatives. TurboCell in the BorgWarner orbit, plus aero engine derivatives repurposed as stationary generators. Everything gets a look right now because buyers are desperate for speed to power.

Stealth entrants. He assumes several exist that have not been announced, precisely because Ceres-style licensing deals do not get publicized.

Asked whether Bloom owns the US market today, his answer: "Pretty much now they do."

> Why Hydrogen Never Worked, and the Read-Through to Plug

Useful because he lived it from the inside. Delivered liquid hydrogen bottoms out near $8 per kilogram. Run that through the efficiency stack and fuel cost alone lands around 54 cents per kWh, before equipment, labor, warranty or service. He stopped modeling at that point. Even at a hypothetical $4 per kilogram you land near 25 cents, still a non-starter against a 7 cent Bloom box.

Plug built a 3 MW unit at its Latham campus that passed Microsoft's full backup generator protocol, the first non-diesel, non-gas system ever to do so. Microsoft publicized it as a breakthrough and then walked away inside six months once the cost picture clarified. Plug's INVISTA facility was outfitted to build stationary modules for the data center market and effectively none of it shipped. Three sites total, including Calistoga in PG&E territory for public safety shutoff backup, and an EV charging site that existed only because a grid connection was unavailable. Both are showpieces that draw tours. Neither is repeatable.

source: Tegus

[The Innovation Investor @OceanusResearch]

Here is Morgan Stanley's response to the short selling report:
Scandium: Hunterbrook’s argument looks substantially overblown.
Brookfield revenue: mostly normal project finance; Hunterbrook’s characterization is misleading.
Project delays: ordinary infrastructure friction, not a Bloom show-stopper.
Backlog/RPO: Hunterbrook’s comparison is too simplistic.

[The Innovation Investor @OceanusResearch]

@baispx Morgan Stanley 就这水平？白银和AI风马牛不相及。看似相近的东西没有因果也没有联系，这之最基本的逻辑错误。可笑可笑。

[The Innovation Investor @OceanusResearch]

@TJ_Research Actuall this is win-win for everybody. Think about the scenario where Meta just build for internal use, $125 billion CAPEX, then with no ROI, hard to convince the board more CAPEX. Now if they are able to monetize, they can actual grow from here.

[The Innovation Investor @OceanusResearch]

@TJ_Research Valuation是标尺，但不是决定因素