This key chart from @SemiAnalysis_ appears to have been the key source for claims of "50,000 Hoppers" and more detailed disclosure on their CapEx buildup analysis ("$1.3B").
But the table has errors/inconsistencies. More significantly, key assumptions don't pass sanity checks.
1⃣ First thing you might notice is that it says 60,000 in the Total column.
But the A100s aren't "Hoppers". So the 50,000 is just the last three columns.
Ok, so far so good.
This is where it starts to get confusing.
The Total column only seems to SUM the first three columns.
Also "ASP" and "per GPU" are average numbers, so you cannot just sum it up. You need to do a weighted average. So the Total figures make no sense.
While the top "# of GPUs" line sums up all four columns, down below only the first three columns are added together.
So the $1.3B capex figure doesn't include the H100s?
Careless formula error? Or do we read more into it?
And then this last line seems to be a sum of Server CapEx and "Cost to Operation".
But TCO (4y Ownership) implies that it should be a per year figure (the label says "$m/hr").
I think it should be the sum of the Server CapEx + Cost to operation divided by 4 but hard to say.
Anyway I put together what I think is a corrected version of this chart if we are counting all 60,000 chips it is $1.6B and $640M p.a. of TCO.
If we are only doing "Hoppers" then it is $1.4B of CapEx and $545M p.a. of TCO.
2⃣ Ok all these might just be chalked up to basic first-year analyst spreadsheet errors and may or may not impact the ultimate analysis.
More substantively ... how credible is the "50,000 Hoppers" estimate in the first place?
The article links to a proprietary "Accelerator Model" that is paywalled so difficult for me to confirm rationale here beyond pure speculation ...
... but what I can do is run a sanity check based on basic understanding of the economics of fund management.
Some people out there are saying High Flyer managed $8B in funds, implictly assuming that those sums could support such a large CapEx number.
But that's not how the hedge fund business works.
What we know about High Flyer and DeepSeek:
▪️ High-Flyer was a quant fund with $8 billion in AUM.
▪️ DeepSeek was "self funded" by High-Flyer.
Can a $7B AUM hedge fund self fund $1.6B of capex? Highly unlikely.
The economics of a hedge fund are typically a management fee and performance fees.
1%/20% is typical.
So on $7B, High Flyer would generate an estimated $70M in management fees.
Performance fees are calculated only on gains. Note below High Flyer's performance of ~13% annualized since 2017.
However, note also how returns have basically been down since 2021. There are unlikely to have been significant performance fees since 2021.
So while strong fund performance through '21 — albeit likely on much lower AUM as it was ramping up — could have arguably funded the reported purchase of ~¥1B in GPUs in 2021, it is unlikely that the hedge fund itself could have continued self-funded that level of CapEx going forward.
$130M is already an extreme amount of CapEx for an $7B fund.
Blackstone, which has more than 100x the AUM (which drives revenue), has an annual capex budget of ~$250M.
Goldman Sachs generates close to 1,000x the revenue as High Flyer, and has an annual CapEx budget of ~$2.5B.
Similarly companies like Alibaba, Baidu and Bytedance generate tens of billions in revenue, orders of magnitude above High Flyer.
They can afford to spend billions buying nVidia chips and building out their own internal datacenters.
There is absolutely no way an $8 billion fund (with flat/negative returns over the period) could have "self-funded" another $1.6 billion in CapEx.
You know what it could have reasonably funded? 2,048 H800 datacenter worth ~$70M ...
... and even here that is quite an extreme CapEx ratio for a fund generating a total of ~$70M (maybe) of management fees that need to pay for fund operations themselves.
So the only possible way that High Flyer could have funded the purchase of another even just 10,000 H800s would have been to have raised secret outside funding for DeepSeek, which of course contradicts the article itself.
Of course, there are now rumors of that swirling around — maybe as people figure out the above math — but then we should just be up front that these estimates are based on pure unsubstantiated speculation and just leave it at that.
A model (even one riddled with basic formula errors) is only as good as its assumptions and it looks like the assumptions here of DeepSeek having access to "50,000 Hoppers" to build out v3 are built on an increasingly shaky foundation.
This is what it looks like with DeepSeek's actual reported cluster of 2,048 H800 GPUs.
These still seem high, but are at least within the realm of reason.
High-Flyer Quant Fund CEO Lu Zhenghe disclosed in an interview in 2020 that "70% of annual revenue is reinvested back into research and development" with strong implication that it is mostly production related, and not CapEx.
P.S. A very common Excel mistake is when you add a column and formula doesn't pick it up.
I suspect this is what happened here: Analyst added "H100" column, Total formula didn't pick it up + while top row is easy to spotcheck, bottom ones were missed
P.P.P.S. Call me when AGI can figure out Excel, amirite @abcampbell ?
P.P.P.P.S. This is just a very quick estimate of the lifetime revenue that High Flyer funds would have generated with accompanying assumptions.
~$400 million available for reinvestment into both R&D and CapEx.
As mentioned earlier, this estimated P&L would support the self-funded buildout of the initial dataclusters (up to 10,000 A100s) through 2021 but hard to see how it could have self-funded anything close to the implied OoM increase in CapEx.
The 10,000 A100s bet was already an extraordinary bet for Liang / High Flyer, with parallels to Elon Musk investing nearly all his PayPal sale proceeds into Tesla + SpaceX.
It's also inconsistent with Quant Fund CEO's comments in 2020 of redirecting reinvestment efforts at R&D (a.k.a. smart people) instead of CapEx.
And yes it makes much more sense that DeepSeek rented from the bigger players and didn’t even own the “2,048 H800s” that they mentioned in the v3 paper.
The H800s that they owned would have been for limited R&D purposes, like trying to hack the PTX code.
So bottom line is I think we violently agree the 50,000 number makes no sense.
@YouJiacheng @angelusm0rt1s @blob_watcher Until they close that loophole
@FarazKh78685502 @dylan522p @SemiAnalysis_ Just to save you the suspense - no Liang doesn’t have a trust fund
The whole China-US supply-demand debate is polluted by shoddy economic reasoning and false narrative premises that have led to piss-poor strategy and policy implementation:
▪️ U.S. demand isn't fixed but driven by income (both wage- and capital-related), which is driven by productivity. Lower productivity — whether from trade war-related economic adjustments or retaliatory actions — will negatively impact income, which leads to lower sustainable demand.
▪️ The U.S.-China bilateral goods balance overstates the surplus as it does not account for offsetting deficits that China runs with other countries, the large FDI income and services deficits and other factors. Thus focusing on this metric has led policymakers to seriously overestimate the economic leverage American "demand" has over China.
▪️ Focus on China's "low consumption" has long been a red herring. It is demand — which like the U.S. and or any economy, is driven by income and productivity — that matters in the long run.
▪️ China's "low consumption" is mainly a function of its gross capital formation (GCF) levels being high. GCF is mostly domestic. And people forget GCF is merely a form of deferred consumption: all economic activity (GDP) becomes consumption at some point; "consumption" and "gross capital formation" are merely differentiated by the question of whether it is consumed now or consumed later. This concept might be less confusing if we properly referred to "consumption" in a GDP concept by its more accurate technical name, "household expenditures".
▪️ China's persistent "low consumption" or "low household expenditures" is not a function of debt or institutional "constraints" but policy choice that is largely underpinned by demographics: China's current labor force complements capital-intensive development. And Chinese housing and infrastructure buildout is not close to being complete.
▪️ That said, the former (labor force priority) is changing rapidly, driven by actual demographic change, automation and the trade war, all of which force costly economic adjustments on the economy.
▪️ The trade war-related adjustments lead to productivity boosts in the long run. Offshoring labor-intensive export processing work that dominates Chinese exports to the U.S. is what China needs to do in the long run to become an advanced, high-income fully developed economy.
▪️ Both the U.S. and China need to undertake costly economic adjustments in the short- to medium-term. The "winner" out of the trade war is the one that can (i) more rapidly undertake these adjustments and (ii) make the right adjustments that lead to productivity growth in the long run.
"U.S. demand isn't fixed"
There are many analysts out there — e.g. those who like to use the phrase "supplier of demand" — who seem to rather ignorantly assume that American demand is some constant, magical force without considering the fundamental sources of real demand, which is productivity and global trade ... and how the trade war might impact both of these.
The American economy is highly productive! A key part of productivity growth, particularly over the last three decades, has been the continuous rise of the American MNC, especially in sectors like technology/Internet and pharma.
American MNCs have gone out into the world and absolutely crushed it. Their rise drives the incomes of well-paid employees (mainly located in the U.S.) and capital income in the form of dividends, share buybacks and rising market capitalizations (which support persistent capital inflows).
Rising incomes support rising demand. American MNCs directly and indirectly enable American households to increase their purchases of physical goods from places like China.
The obvious corollary to this is that any relative decline in such a key source of rising American incomes will also correspondingly impact American demand.
For example, if China undertakes retaliatory action against American MNCs operating in China, generating hundreds of billions of dollars of revenue selling to Chinese households (not counted in the trade balance, by the way), then this will impact American income (and demand) in exactly the same way that the Trump tariffs forcing Chinese exporters to make painful adjustments.
American share of global demand would have to decline until American MNCs can find new markets, just like Chinese share of global supply would decline until Chinese exporters adjust their business models.
It's exactly the same, just involving different types of companies and reflected in different categories on the Balance of Payments.
Simplest way to think of Huawei is that it has first pick (de facto monopsony) on elite STEM talent from the largest pool of STEM talent in the world and a culture and organizational & incentive structure that can efficiently re-allocate its R&D workforce to new product development adjacencies.
The NBA lottery is coming up so I will use that as an analogy.
From this perspective, it is analogous to a company like Samsung Electronics and TSMC — which hold similar “draft rights” on elite Korean and Taiwanese STEM talent** — but scaled up 20x and 50x, respectively.
** the Korean and Taiwanese equivalents of Cooper Flagg, Ace Bailey, VJ Edgecombe and Dylan Harper …
And in the United States, it is similar with Big Tech.
Companies like Google and Amazon can also expand into new product and service adjacencies because their financial gravity and brand prestige with prospective hires mean they have a disproportionate number of “first round picks” every year.
And on top of homegrown talent, the U.S. has the added benefit of attracting international STEM players through its leading university system.
China exports ~100M iPhones p.a. to the U.S. currently. This is valued by customs at ~$45B, or ~one-tenth of exports.
China’s economy only makes ~$60 in net GDP per average iPhone 👇 (this one says $72 but this is a high-end iPhone).
So China needs to replace ~$6B of lost GDP, or 0.033% of its annual output. At ~$8/hour, this is ~300k (mostly blue-collar) workers.
Every day, on average China creates ~33k urban jobs (~12M annualized). So losing the iPhone trade amounts to ~9 days of job absorption.
Remember this is ~one-tenth of China’s bilateral exports to the U.S.
So you need to explain to me why China won’t be able to absorb the loss of these relatively low value-add export processing jobs? If anything, it will merely just accelerate its move up the value chain.
Instead of assembling iPhones, China will grab higher market share of higher-value components like chips, memories and OLED screens.
Now think about what happens when Apple loses market share, either as a result of retaliatory actions on its China sales or by tariffs rising prices and lowering demand.
Apple sells ~45 million iPhones in China today at ~$900 per unit. It generates ~43% operating margins, or ~$390 per iPhone.
Say Apple loses half its market share in China. ~22M iPhones x $390 operating profit per iPhone = ~$8.5B in lost economic value to Apple / U.S.
Who is hurt more in this exchange?
This market share is most likely absorbed by higher end domestic phone models from Huawei and Xiaomi.
Instead of spending $900 on iPhones (where only $60 of manufacturing value-add accrues to the local economy), Chinese HHs now instead spend the same amount on domestic phones where most of the price supports the Chinese economy.
China effectively swaps low-value add manufacturing value-add with much higher value-add tech and R&D value-add.
I think it’s a trade they would make.
The only downside is the economic adjustment, which is a “one-time” event.
A decade it was virtually inconceivable that the relationship between the U.S. and EU would splinter so much that it gave China the opportunity to fill the void.
This would be the trade/economic equivalent of the U.S. splitting China off from the Soviet Union in the 70s.
When Xi Jinping makes statements like "the world is in a turbulent time that is unprecedented in the past century" in 2021 he was likely referring to the possibility that situations like this to arise where China is essentially being handed the opportunity to re-shape global affairs much earlier than anticipated.
This is a complete self-own by the United States — in particular, a Trump administration whose crusade against allies and poorly executed implementation of the latest "reciprocal" tariffs are increasingly being perceived as an "Emperor Has No Clothes" moment for the President and the U.S. nation.
I don’t think people in replies making statements like “Korea will refuse to comply” quite realize that “ask” is a polite prelude to “also ban all RE shipments to Korea due to non-compliance”.
Just as China was forced to adapt to chip export controls by stockpiling restricted equipment, exploiting loopholes, smuggling in the short/medium-term + developing domestic capabilities (upstream SME, building out domestic chip capacity) in the long-term …
… now the U.S. will also have to adapt by resorting to similar efforts on rare earths … smuggling, loopholes, stockpiling in the short/medium run and building out rare earths refining and processing capacity in the long run.
I find it quite ironic how Pettis tries to distance himself from the Trump administration's tariff debacle given the clear influence his thinking and narratives have had in shaping the current obsession with tariffs; in particular, the disproportionate focus on the goods trade.
We can clearly see this focus on the goods trade in the article's headline suggestion/recommendation of implenenting a "customs union like the one proposed by the economist John Maynard Keynes at the Bretton Woods conference" that attempts to enforce balance in (goods) trade.
The problem with this recommendation is that global trade in the 2020s looks very different from global trade in the 1940s, which was almost entirely based on physical trade flows of manufactured goods and commodities, as illustrated in this diagram 👇.