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 state cannot allocate capital more efficiently than the market.”
An oft-repeated axiom chanted like a religious mantra and accepted by many as a universal truth.
But one that can be easily debunked with a straightforward contra-example from one of the most capital-intensive industries of them all: passenger rail.
China Railway (SOE) vs. Brightline (private)
CR HSR:
▪️ 48,000 km of greenfield track, predominantly elevated on viaducts
▪️ Serves 3.6B passengers annually
▪️ ¥550B of revenue on ¥5T of capital investment (9 years revenue payback)
▪️ 42 fatalities over 17+ years and 23B passenger rides
Brightline Florida:
▪️ 376 km of refurbished at-grade track
▪️ Serves 2.8M passengers per year
▪️ $187M revenue on at least $5.5B capital investment (29 years revenue payback)
▪️ Caused 182 fatalities in two-plus years of operation (hint: maybe you shouldn’t run fast trains over at-grade crossings).
Did the private company really do a better job allocating capital here? (rhetorical)
So no, I don’t think “the private sector is always better at allocating capital than the state sector” should be simply accepted as a universal truth, unchallenged.
It depends on the industry and the type of capital formation and the level of state/institutional capacity.
The more interesting, less-ideological exercise is to figure out the optimal ratio of state vs. private involvement on a sector-by-sector basis. This one requires actual nuance and complex thought.
Re-invigoration of biking culture in China and the relentless expansion of dedicated biking lanes today can really be traced to the invention and proliferation of dockless bike-share systems starting around a decade ago.
~830B barrels of proven reserves in the Middle East has effective energy equivalent of around 11,300 GW of solar PV that produce over a 25-year useful life.
At 14 km2/GW, this would take up desert space of ~158,200 km2, which is less than a quarter of China’s portion of the Gobi Desert.
Moreover, regular maintenance and replacement means this infrastructure would produce energy in perpetuity, while the Middle East oil fields run out or become more costly/difficult to extract (even with improved extraction technology).
China is currently deploying solar PV at a run rate of 300+ GW per annum, which means at just current run rates it can deploy this volume of solar PV in 37 years.
Remember it also took multiple decades to develop the vast oil fields in the Middle East starting in the 30s and 40s.
I think foreigners — especially Americans — do not fully appreciate China's predilection for large, capital-intensive infrastructure projects because most do not know what it was like to live in a place starved of God-given natural endowments.
While we marvel at the economic benefits of a navigable Mississippi River system, bountiful arable land enabling "amber waves of grain", and "purple mountain majesties above the fruited plain" and rich stores of oil & gas + other useful commodities ...
... China trudged through the 20th century in relative poverty, cursed by a dearth of natural endowments like arable land and commodities relative to its huge population.
But it recognized the potential for capital-intensive infrastructure development to convert non-productive regions to productive ones.
I had written a deep dive on known issues in the measurement of China’s GDP and how misleading it was to frame the discussion around the GDP accounting identity, especially if the way those numbers are calculated differed wildly from country to country.
In light of the recent discussion of China’s under-counted consumption 👇, it was worth re-upping these pieces.
Part I provided relevant background on the technicalities of GDP measurement and the historical development of Pettis’ “Over-investment Thesis” and the critical role of the GDP accounting identity on determining “imbalances” in China’s economy.