April – 1805

Napoleon is master of Europe

Only the British fleet stands before him

Compute is now an asset class

I see it is once again time to talk financial innovation. Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR are all working with Nvidia to put together $500 billion in financing to turn compute into an asset class.

“This is really the first time that technology chips have become an investable asset class,” Nvidia CEO Jensen Huang said to CNBC. “These are revenue-generating assets now. They’re productive, they’re long-lived, they’re fungible, they’re flexible.”

Huang said something very different about Nvidia’s own last-generation Hopper chips last year. “When Blackwell starts shipping in volume, you couldn’t give Hoppers away,” Huang told attendees at the company’s AI conference, hyping up its latest GPU architecture. “There are circumstances where Hopper is fine. Not many.” So to now be told that chips are actually “revenue-generating assets” that are “long-lived” is… quite frankly, it’s giving me whiplash. 

At least for right now, Huang isn’t wrong. The price to rent old chips has been rising, and Silicon Data projects that it will continue rising through 2028. Here’s a fun anecdote: One cloud service provider nearly doubled its prices on Nvidia Blackwell B200 chips for one rental customer during its contract renewal.

“This is the very beginning, like what it was when I started in the mortgage-backed securities market in the 1970s, and I look upon this as a next future for financial engineering,” said Larry Fink, CEO of BlackRock, to CNBC. Now, for some of you, this may make alarm bells go off. As former hedge fund manager Mark Rubinstein notes, mortgage-backed securities failed when mortgages were overproduced. The AI industry is becoming saturated with data centers, and Chinese open-source models require less compute despite being fairly powerful, both of which seem like potential threats to the notion of ever-growing demand for chips. There is also a far more basic question: Can frontier labs such as Anthropic and OpenAI, which are driving much of the current demand, make money?

Before we even get to the Jensen math, I want to point something out: This is not a done deal. This is some memorandums of understanding. You may remember that last year, Nvidia signed a $100 billion memorandum of understanding to invest in OpenAI. You may also remember that it, uh, didn’t happen. But the cool thing about memorandums of understanding is that you get to make a big announcement, and then it sort of doesn’t matter if the actual thing goes forward. Still, let’s assume it’s real, because even as a trial balloon, it’s telling us something interesting.

Putting the ass in asset

Let’s back up for a second. Why are we talking about “compute”? Well, according to Huang, “Nvidia compute is not just a chip.” That’s because there is also software, called CUDA. “That is what makes Nvidia AI factories different” from mere dumb silicon, Huang says in a tweet — er, post on X. “Their value is not fixed at installation: CUDA continuously improves their output; the installed base remains productive well beyond its initial depreciation period.”

Okay, but the chips and software alone don’t create compute — they’re only useful if they’re housed in massive data center infrastructure, which requires warehouses and power supplies. Huang appears to be discussing compute without those things, dubbing Nvidia’s system “a complete AI factory platform including accelerated computing, networking, systems software, AI frameworks and a global developer ecosystem.” Notably absent from this list: brick-and-mortar facilities.

Leave aside the risible idea of an “AI factory,” where electricity presumably toils in the silicon chip mine. Huang is downplaying data centers partially because that’s where most of the financing has gone so far. “Blackstone has built a platform valued at $185 billion including facilities under construction, and reckons the market for long-term ownership of stabilized data centers could grow to $1 trillion over time,” writes Rubinstein. Huang doesn’t care about that — a lot of it is real estate and irrelevant to him. Huang cares about people buying Nvidia chips.

So “compute” here isn’t referring to the entire data center stack; it’s a buzzword-y way of talking about our old friend, the GPU-backed loan. I can see why one might want to switch to “compute” over “GPU” because everyone knows that a GPU has a much shorter lifespan than, say, a building — estimates range from somewhere between two and five years. I suppose “compute” also covers TPU-backed loans, so there’s that.

Earlier this summer, Broadcom put together a $35 billion package that looks an awful lot like what Nvidia is offering now, signing a deal with Apollo and Blackstone to fund what we are now calling compute, with about a million chips as collateral. Apollo and Blackstone will make money on interest; Broadcom has provided a guarantee for the two senior notes issued by the special purpose vehicle where the chips live. This deal was meant to boost demand for Broadcom chips. It seems like Nvidia took note — and is doing the same thing, for the same reasons.

So now Huang is cheerleading the long life of Nvidia chips. As a “powerful example” of how compute can improve over time, Huang points to the pre-Hopper A100 chip, which it introduced in 2020, and which “remains in active commercial use,” he says. “Customers continue to commit capacity for multi-year deployments, extending A100’s economic life toward a decade.” My goodness, that’s very different from what he said last year about his flashy new chips, isn’t it! 

We’ve talked about chip financing before around these parts. You may remember that no one can agree on a depreciation schedule for chips; it sort of doesn’t matter as long as Nvidia wants to bail out the companies that buy them. You can, in fact, view Huang’s statement as a sort of bailout itself. In the discussion about chip depreciation, short seller Michael Burry has suggested that two to three years is the appropriate depreciation cycle for chips. IBM’s Arvind Krishna says depreciation takes five years. And here comes Huang, saying the economic life of one of his chips is a decade! My, my, my.

This is relevant to the lenders, because it determines loan terms. For instance, the amount that CoreWeave — the pioneer of GPU-backed loans and an Nvidia client state — can borrow decreases as its chips depreciate, according to its corporate filings. So if Huang is out here in front of God and everyone saying that the depreciation schedule is 10 years, then I don’t see why banks wouldn’t believe him. That’s pretty useful for anyone trying to get loans from this consortium, I figure.

Huang cites price increases on compute — including for the Hopper H100 chip, which came out in 2022. He’s not exaggerating about the price increases, as self-serving as his logic may be. They’re driven by a higher demand for inference, which is the industry term for when a trained model analyzes new data, according Brendan Burke, an AI industry analyst. That meant the hourly rates for old chips remained high, and in some cases, even increased, Burke says. “There’s just been a major shortage of inference chips, and that’s reversed the expected trend of decreasing prices,” he told me.

On CoreWeave’s second quarter earnings call, CEO Michael Intrator said that the company has been able to sell GPUs with architecture from 2020 in a contract that extends through 2029. Connecting the dots, since CoreWeave is so tightly wound with Nvidia, I wonder if this is what Huang’s decade depreciation cycle refers to.

And right on cue, CME Group, a derivatives exchange, has announced its plans to introduce compute futures in October, assuming the regulators approve the two contracts in question.

Will the demand surges go on forever? Fuck, I dunno. There are all these data centers being built, and it kind of seems like if compute is (or rather, chips are) as fungible as Huang says, that means data center providers are competing on price in a saturated market. But as AI gets integrated into more things, more normal companies — on top of frontier labs — will need to run inference. The pace of adoption matters — if it is too slow, this model may run into trouble.

Our fearless leader Nilay Patel has been running around with his hair on fire in Slack, asking how it is that if you put a dollar into compute, you get $1.01 back. Huang does not exactly answer this question: “The return is in the usefulness of AI,” he writes. But if my understanding of what’s going on is right, and “compute” in this context is just the old, familiar GPU-backed loan, then the return on investment is what it usually is with debt: interest.

So there’s that. We also don’t know what the contracts look like, and the details matter. (In the Broadcom contract that appears to have inspired Nvidia’s announcement, Broadcom is not backing all of the debt, just the higher-priority senior debt, for instance.) Based on previous GPU-backed loans, I’d guess that the contract from whoever is buying the compute is included among the collateral. That contract is better or worse based on who’s behind it — Microsoft will surely pay its bills, but OpenAI doesn’t make money and needs to keep raising, so its contracts are riskier for lenders. Plus, in any agreement, it’s possible that there might be a clause in there giving the debt providers some kind of revenue share or other way of sweetening the deal. What I do know, though, is that this new compute consortium seems like a pretty good deal for Nvidia.

Competitive landscaping

Last year, when I talked to Stanford University’s Vikrant Vig, he noted that the majority of GPU loans were made with Nvidia chips as collateral. That, in turn, made it easier for companies to get new loans with Nvidia chips than with competitors’ GPUs — the cost of financing Nvidia GPU loans was lower because the collateral is more liquid. If the deals between Nvidia and the financiers do get finalized, that will make it even easier to get financing for Nvidia chips. If you’re starting a neocloud — that is, a small company that rents out compute such as CoreWeave, Crusoe, and Lambda — from scratch, buying Nvidia chips gives you support that you can’t necessarily get from competitors such as, idk, Broadcom. 

“In effect, they made Nvidia’s product cheaper without really cutting GPU prices,” Felix Wang of Hedgeye Risk Management told Bloomberg. 

Nvidia has been aggressive about investing in and providing financing to neoclouds in order to expand its customer base. By funding and nurturing neoclouds, Nvidia reduces the bargaining power of the big boys (e.g., Microsoft, Amazon, Google, and Meta) on price. Interestingly, on its most recent earnings call, SpaceX — the big new neocloud player — said it was working exclusively with Nvidia chips; later, we all discovered that Nvidia had a $21 billion stake in SpaceX. SpaceX was evaluating alternatives to Nvidia, but its data center buildout requires a massive increase in spending — so if Nvidia’s investment may have locked the neocloud in.

But there’s also another interesting side effect of this financing, points out Burke. Because it’s in the interests of lenders to have relative uniformity between the loans, that may further standardize the way Nvidia chips get installed in data centers. That may also give Nvidia a competitive advantage in selling chips. 

It turns out that GPUs perform differently depending on how they get set up, which can make it hard to reliably project revenue for the lenders taking on the risk, Burke says. Nvidia has started putting out guidance about revenue in the ideal setting, pushing cloud computing providers to use that particular design. That would provide standardization, making lenders’ jobs easier. It also invites more scrutiny on how much customers can make and the accuracy of Nvidia’s modeling. “The forecasts I’ve seen are very bullish,” he says. In some cases, the projections are for $70 billion a year in revenue per gigawatt, which is not what anyone in the field is getting today.

So the terms of the contract may demand specific settings that increase the fungibility of data centers with Nvidia chips, both because it makes it easier to model revenue forecasts and because in the case of a default, that makes it easier for lenders to offload the collateral. “Most data center operators are highly customized and it’s going to be a major sheep herding exercise to get them to follow one approach,” Burke says. Conditions on lending may serve as sheepdogs, corralling the engineers into specific designs.

This also shores up Nvidia against competition — and not just from Google’s TPU and Amazon’s Trainium chips. Inference can be run on old Nvidia chips, sure, but it turns out CPUs can also do this work and CPUs are cheaper — like, one-fifteenth of the cost, Burke says. So if you can use CPUs, you not only can spend less to buy chips, you can also lessen the demand for GPU compute, driving that price down.

Don’t call it circular financing

Nvidia is bringing in outside capital because it appears to be quite sore about the accusations of “circular financing,” where it’s a major investor in the neoclouds and AI labs that buy its chips. Remember CoreWeave, the neocloud propped up by Nvidia? Nvidia invested and saved its IPO and has promised to buy any extra capacity CoreWeave might have. Nvidia “agreed to spend $1.3 billion over four years to rent its own chips from CoreWeave,” making it CoreWeave’s second-largest customer in 2024.

It’s not just CoreWeave. Nvidia is widely invested in the neocloud companies. Plus, Nvidia is paying $30 billion in cloud service agreements as of its most recent quarterly filing. Jay Goldberg, a senior analyst at Seaport Research Partners, thinks these numbers represent Nvidia’s backstop agreements.

So if the new memorandums of understanding are finalized into deals, we wind up with a different situation. Instead of (say) Nvidia giving CoreWeave a dollar, against which CoreWeave borrows five dollars and then buys six dollars of Nvidia chips, Blackstone is giving CoreWeave five dollars to buy Nvidia chips. 

Let’s look again at who’s in this consortium, shall we? We’ve got Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR — so exactly one bank, Goldman, and a bunch of private credit companies. Private credit has been financing the AI buildout in a big way.

I am not an expert in finance but it seems to be the case that private assets make more money for the finance bros than public ones. Because AI has been on the rise, there’s been a large-scale freakout in private credit about the threat from AI to software as a service, a sector that a bunch of private credit funds had gotten into pretty deep. So now there’s a new interest in asset-backed securities, and a loan that’s backed by AI chips has the virtue of (1) not being an SaaS company’s debt and (2) potentially being the infrastructure for the thing that kills the SaaS company. That might make GPU-backed loans look attractive.

The AI buildout generally has pivoted to debt. As of the end of July, the hyperscalers “and related companies” like Nvidia had issued about $225 billion in bonds, according to S&P Global. “This segment has seen almost 10-fold growth in its bond issuance through midyear,” S&P noted, and could issue $400 billion by the end of the year.

What’s more,  the hyperscalers have made $1.5 trillion in lease commitments — and $1 trillion of it isn’t on their balance sheets. There’s also an estimated $1.5 trillion in purchase commitments for chips, electricity, and so on. So, you know, a lot of liabilities.

Apollo, at least, has seen a big opportunity; The Information reported that there’s a new guy there in charge of AI infrastructure financings. Arranging AI deals is “a growing source of revenue.” About 60 people are focused on AI buildout. The development of the data centers themselves is financed differently than the chips.

I am pointing at Apollo because it published something interesting recently. In it, Torsten Slok, the firm’s chief economist, notes that the further away in the AI stack you are from the end user, the bigger your profit margin is. Models and applications lose money. That’s not a problem forever — Amazon lost money before it made money, for instance — but it adds another layer of risk to these loans. The neoclouds are the layer one up from models and applications. Should those companies be unable to figure out how to turn a profit, they are directly exposed to the risk.

AI evangelists compare AI to the internet, as a technology that has the power to totally reorganize their society. Curiously, a lot of these evangelists do not have a good model for what AI’s goals should be. There’s a lot of talk about curing all diseases and “intelligence as a utility,” but the most concrete ones look like “replace customer service agents” and “speed up coding”; maybe there’s some room for, I don’t know, risk assessment in insurance and stock trading. Are those applications enough to justify the enormous capital outlays we’ve all seen? I doubt it.  

By contrast, in the early days of the internet, the technology wasn’t ready to deliver streaming music and video, but by 1999 it was obvious to a lot of people, including Larry Ellison, that’s where things were going. Similarly, in the ’90s, we weren’t culturally ready for online shopping, but it was clear to a lot of people that was an opportunity. The problem of the dot-com bubble was not that the evangelists were wrong about what the tech could do — it was that they were wrong about when the tech could do it.

So even if the most ardent AI boosters are right, getting the timing right also matters. The entire model ecosystem is currently being subsidized. It’s not yet clear that if the model makers were to charge the actual price for their services that their demand would be there. Imagine a perfect AI personal assistant, trained on every document in your organization; it costs $100,000 a day. Even if it is very good — nearly perfect! — it is way more cost-effective to hire 100 people who cost $200,000 a year. 

Compute is only revenue for companies running cloud platforms, points out Larry Dignan at Constellation Research. For everyone else, it’s a cost. And companies are always under pressure to contain costs — consider the big splash that Uber made in May when the company’s president said it was getting “harder to justify” the amount it was spending on AI.

OpenAI and SpaceX hemorrhage money. Anthropic has recently been reported to have an annualized run rate of $65 billion — but there’s no word on profit. There’s increasing pressure on the model companies to make money, and to get to a 7 percent return, below which is an “unmitigated disaster” for AI investors, the economic forecasters at Gartner project that AI companies need to cumulatively earn $7 trillion in revenue through 2029. That’s almost $2 trillion a year.

Now, if Fink is right, and compute-as-an-asset is comparable to mortgage-backed securities, we should expect a lot more companies to jump into arrangements like the one Nvidia is touting and Broadcom actually arranged. But if any of the major AI model companies suddenly go belly-up, perhaps because they cannot make a profit, the demand for compute abruptly drops. What’s more, if their contracts to rent chips are part of what secures collateral on chip-based loans, those loans are also in trouble.

One sign that Nvidia is more bullish on “compute as an asset class” than the financiers it’s signed memorandums with is its residual value support. Basically, if the neocloud bails on the loan, Nvidia has agreed to pay up to 25 percent on some of these contracts. This is perhaps meant to reassure investors, since Nvidia’s assets are on the line for investments for companies with little or no credit record. The financing suggests “that Huang believes his ‘investable asset class’ pitch much more than the market does,” says Stratechery’s Ben Thompson.

And although the price of compute has gone up, it’s irrelevant to the question of residual value, which is the resale price of the chips. If someone goes belly-up on a loan, and, e.g., Goldman has a bunch of compute to flip, who’s buying and for how much? The big boys are all building their own data centers, and in some cases using their own proprietary chips. The neoclouds have a ton of debt to service — they may not have the cash to be buyers. That’s not settled. So if the resale value falls below a certain level, Nvidia has to compensate whoever owns the debt.

Jensen math

We don’t have a lot of details, but what will matter here is how much the Nvidia GPUs are sufficient collateral for lending, says Goldberg. In most deals so far, the chips alone weren’t enough — lenders also needed contractual cashflow on those chips. So CoreWeave’s GPU loans are really backed by Microsoft or Nvidia or whoever. If the “AI factory” only needs chips as collateral, and not customer contracts as well, that’s significant. But notice: Jensen Huang isn’t saying that directly.

“Welcome to Jensen math,” says Goldberg in an email. “Jensen is now trying to claim that this is a new investment class – stocks, bonds, mortgages, GPUs. And his tweet is arguing that this is a special asset class because it somehow gets better over time because of software, magic and reasons.”

At a certain point, it begins to feel like this is another way for Nvidia to keep the AI party going. It’s been on a historic run, and investors’ expectations for it are high. Nvidia may have been facing limits on its previous model of endless upgrade cycles, says Leevi Saari, a fellow at the AI Now Institute. “Previously, they were like car salesmen, saying you need a new car every year because the previous generation was so inefficient you’d lose value.” Now, Huang seems to be saying that depreciation doesn’t matter — compute doesn’t lose value quickly, like a car. It loses value slowly, or even gains value, like a house.

The number of companies that can afford to keep buying new Nvidia chips every year is limited, Saari says. For Nvidia to keep beating and raising expectations on its earnings, it has to unlock more ways to fund companies buying chips. Enter the financial institutions it’s cut a deal with. If the chips don’t depreciate the way Huang said they do just last year, they’re suddenly assets that might interest, say, pension funds.

“For the life of me I can’t fathom how they square the circle of ‘you must buy a new chip every year’ with ‘don’t worry about depreciation,’” Saari says. To believe that chips will continue to appreciate in value, you have to believe there’s a totally inelastic market for chips. “That’s naked sleight of hand, in my opinion.”

According to Saari, the actual financial innovation is “how do you unlock safety-seeking capital for Nvidia’s revenue growth” and the answer is the announcement we all saw. The market’s response was muted. In The Wall Street Journal, Jack Ablin, a founding partner at the $260 billion family office Cresset, which invests in Nvidia, noted that compute, historically, is “an asset that’s had the shelf life of lettuce.” Even Stratechery’s Thompson, usually an unabashed cheerleader for tech industry pablum, noted that Nvidia’s strategy was risky. 

And who bears that risk? Whoever winds up with the notes issued by these arrangements — and that’s often not the people originating them. “This is originate-to-distribute, and the destination is the general account of a life or annuity insurer,” writes Sascha Steffen, the DWS senior chair in finance at the Frankfurt School of Finance & Management and the director of the Centre for European Transformation, a research group focused on private credit. 

There are a few interesting things here. Half of the group making these deals shares an owner with the entity likely to wind up with the loans. That weakens the scrutiny the final holder, probably an insurer, has on the loans. “Nvidia’s announcement is routinely described as resolving the ‘circularity’ of the company financing its own customers, and at the level of Nvidia’s balance sheet it does,” writes Steffen. “One layer down, a second circularity has been created.”

One other thing: The amount of capital required to cover insurers’ risks is tied very closely to debt ratings. If an insurer is downgraded because, let’s say, a ratings agency makes a change in methodology, that insurer may be forced to sell their GPU-backed loans, “an asset with almost no secondary market,” Steffen notes. 

The question now is whether the actual arrangement will really come to pass. After all, Nvidia has made public pronouncements before — and then shied away. Just ask OpenAI about their Ohio data center.

Recently, a man I was rock climbing with told me about how he’d used AI to make a motivational poster for himself, which he’d hung on his bedroom wall: a bear, walking a slackline over a canyon, holding a sign that said, “Do cool shit.” I made what I hoped was a polite noise. What reaction was this man looking for? He probably hoped someone, maybe me, would say it was cool or inspiring. It seemed I was not the first person he’d told about this poster. “You know, it’s funny,” he said to me. “No one ever says anything about it.” 

I have been thinking about this now for several days. There’s a sort of turducken of things that I find off-putting all rolled into this anecdote: hippie shit, motivational posters, AI “art.” Still, the sum total of reactions to this poster appears to be polite noises.

But what is the correct answer to someone showing you their slop? No one made it. Even bad art — for instance, a version of that motivational poster drawn by a person — requires effort on the part of the artist, and expresses the artist’s mind, both in the content and in the motor skills required to draw lines on a page. It requires a person’s time and attention. AI “art” requires typing a prompt into a box, and maybe refining the results a couple times. There’s nothing to react to.

I bring this up because Mark Zuckerberg has posted a manifesto that he — or his communications team, or possibly an AI — wrote. The point of this 6,500-word essay is to define a positive future for AI. It strikes me as an inauspicious sign for AI that a new, supposedly world-changing and value-creating technology should need such a defense, but AI has been getting bad press these days. People hate data centers, and the higher electricity bills that come with them. Anthropic CEO Dario Amodei has said the technology may cause “unusually painful” job losses across multiple industries. OpenAI CEO Sam Altman’s house was the target of, first, a Molotov cocktail, and then gunfire. 

“It is surprising that the discourse from many developing AI is so filled with doom,” the essay says. “I do not understand why anyone who believes that AI will eliminate most jobs and much of humanity’s relevance would rush to build that future.”

I find it difficult to believe that Zuckerberg doesn’t have Amodei’s phone number. Or Altman’s. I know he has Elon Musk’s — I’ve seen their texts in court. Perhaps he should have texted them to ask!

Anyway, Zuckerberg is trying to be the soothing tech guru, here to tell us that things are going to be okay. He’s addressing, however belatedly, the rage that many people feel about AI. There are sections on data centers (Meta is building power sources for some data centers in the future, but don’t ask for renewable energy) and “preventing government tyranny.” This essay exists to try to quiet the AI backlash — even if it never quite says that directly.

We will leave aside the question of “superintelligence,” which is Zuckerberg’s preferred term for what the rest of the industry calls “artificial general intelligence”; let’s stipulate, for the purposes of this essay, that it is indeed possible to create. I am also going to ignore the risible defense of the pervert glasses contained in this piece; they are indefensible. As for the risks, I think they are best addressed elsewhere. What I am interested in is what Zuckerberg — or whoever is writing for him — believes will improve all our lives.

Here is his first pitch: “Everyone will have an exceptionally capable personal agent that understands you, your goals, and everything you care about. Your agent will work 24/7 on your behalf to improve your relationships, health, career, finances, home management, hobbies, and more.” I am struck, here, by the inclusion of “relationships” and “hobbies.”

What strengthens a relationship? Almost always, it is personal investment. Perhaps an AI agent might be better at predicting what my father will want for his birthday than I am, but it definitionally cannot give him my time and consideration. Love is not just a feeling; it is a way of paying attention.

Zuckerberg uses an example that is telling, in a grim kind of way: He asked an AI to pick out a “personalized” recipe to bake with his daughter. He could have considered what kind of baking his daughter enjoys, what her skill set is and what he might be able to teach her, then picked a recipe. Maybe he would have picked a bad recipe, and they would have bungled it — but that might have been a fun experience and a good story. Or maybe he would have picked a family recipe, and it would have given him the opportunity to tell her about when he was a child, or what his grandmother was like. Certainly he’s a busy man, but wouldn’t that make the gift of his attention to her hobby even more precious? Perhaps he might have come to understand her better by spending more time thinking about what she likes. Without that attention, the baking they do together seems more focused on accomplishment than quality time. 

Relatedly, the point of a hobby is that you do it. Granted, many of my hobbies are physical activities — hiking, running, yoga, rock climbing (with guys who make bad AI art, apparently). But an AI cannot read a book for me, because the activity of reading a book is absorbing its words into my mind; a summary doesn’t do the same thing as the opening lines of Renata Adler’s Speedboat: “Nobody died that year. Nobody prospered. There were no births or marriages. Seventeen reverent satires were written — disrupting a cliché and, presumably, creating a genre.” The summary cannot summon the mood, nor the actual pleasure of reading the words themselves. An AI cannot replicate the soothing quality of knitting my own scarf; it is the knitting itself that soothes.

Zuckerberg suggests that using an AI to assist me in these tasks will let me accomplish more, and that is probably the case. But I always thought the point of a hobby was to relax and enjoy myself. It’s certainly not to maximize productivity. And Zuckerberg’s vision of maximizing productivity comes at the cost of my experience.

The second thing Zuckerberg presents is “Everyone will have incredible tools for creation to express your ideas.” I would suggest that everyone already has incredible tools for creation to express their ideas, and that one need merely practice with those tools in order to get better at them. The pleasure of, say, learning to make a video is, first of all, making the video itself — who hasn’t had fun goofing around with friends while filming? But it is also getting better at the tools one uses to make the video, and looking back at one’s efforts as one’s directorial eye improves. Sure, this is time-consuming and requires effort — it can’t be done in a single evening — but isn’t that what makes it rewarding? 

Similarly, Zuckerberg introduces the unimpressive notion that everyone gets “powerful tools to create new businesses.” This is the guy that had every resource anyone could wish for at his fingertips and his metaverse still flopped. Maybe more ideas can turn into businesses, but not every idea will be a successful business, and the sheer volume of competition may make it much harder to succeed. That is, assuming those powerful tools work as promised. After all, just recently, Meta announced that its AI had hacked another company, following similar incidents from OpenAI and Anthropic. Not only will your own personal company have to deal with such hackers, you may inadvertently engage in corporate espionage yourself. Is that really what every entrepreneur wants to sign up for?

Let’s move on to the next absurd idea the manifesto introduces: Everyone gets a “personalized tutor and coach with a PhD in every subject.” Again, for the sake of this essay, pretend that is possible. We have already tried computerized learning, in 2020, and if I recall correctly, it was a spectacular failure. People don’t like it. Kids don’t like it. Teachers are still coping with the fallout. And I hate to do another victory lap on behalf of Aristotle, but it sure seems like having a mentor (phronimos) is really important when it comes to learning, ethical or otherwise. The social part of learning — whether that’s the peer pressure of being in a class, or the need to impress someone you admire — seems indispensable to the experience.

The strongest thing that Zuckerberg suggests, in my view, is the notion that everyone can benefit from and contribute to scientific advances. Now, I benefited from the polio vaccine — among other scientific advances — at least in part because Jonas Salk chose to make it available to everyone without earning any money from it. But never mind that. I am curious about what it means to contribute to scientific advances in this sense.

Zuckerberg gives some vague handwaving about personalized therapies, which have long been a goal in medicine. Again, let’s assume AI can even do this. I notice, however, that personalization is possible only when doctors have enough time to actually listen to their patients. It also means that patients will need to have the means to pay for personalized medicine, whether through their insurance or otherwise. But this bit from the essay is vague enough that he may actually mean that AI will allow researchers to harvest samples from all of us and test things more quickly, which has its own haunting privacy implications, especially coming from Mark Zuckerberg.

For his final point, Zuckerberg outlines the current state of affairs: Everyone is equal in AI but some people (the ones with money) are more equal than others. The best models will remain paywalled, accessible only to the rich. But what if everyone had free access, he wonders. You mean like they do right now, Mark? How on earth does making the status quo permanent improve our lives?

Consider all this against Terence Tao’s narrower vision of what AI is for in mathematics. He asks us to imagine the following statement is true: “AI tools will, reasonably soon, become capable of performing a reasonable fraction of research-level mathematical tasks, with reasonable levels of success, quality, supervision, and cost.” Tao then stops and asks a crucial question: What are our goals?

It is the question that Zuckerberg’s vision of the future doesn’t ask. For each thing Zuckerberg outlines as a possibility, the presumed outcome is some kind of accomplishment. The possibility of pleasure, of experience, of self-knowledge or self-expression are missing from this vision. 

In contrast, one of the possibilities Tao outlines for the field of mathematics — if an AI can simply solve mathematical problems at will — is that its goal is to help people understand and do math. This accomplishment is perhaps somewhat squishier than “solve a problem,” but only because it is more profound. It speaks to what we understand knowledge to be: not merely the collection of answers to questions, but the understanding of why those are the answers — a deeper way of connecting with the world and with ourselves. This sounds suspiciously like the humanities, doesn’t it? 

Look, I am coming to believe we need a better quality of AI booster, because while I understand the Zuckerberg statement exists mostly to cheerlead Meta’s own AI investment, I have heard similar things coming from people who don’t have similar financial interests. Is the point of life merely to stockpile accomplishments? If that is what Silicon Valley believes, no wonder all the billionaires seem so unhappy all the time! They have traded experience for accomplishment, and cheated themselves out of their own lives in the process. They keep chasing more money and more power, because they think that will finally be enough — but as long as they do not experience things themselves, nothing will ever be enough.

When I am reading a book, or listening to music, or watching a movie, I am not just consuming a resource. I am experiencing other people’s minds. Artists have signature styles not merely as branding exercises but because they are expressing something about how they view the world, what they think beauty is, and what they believe to be the point of art. In prose, word choices are a kind of thumbprint for a specific person. In music, phrasing is another. In visual art, brush or pen strokes. Part of the point of the encounter is the aesthetic vision; the quality of another person’s attention. Part of it is your own attention: Does the artist’s aesthetic speak to you? What have you learned about your own aesthetics from it? What does beauty mean to you? What is pleasure?

If AI can do all the things we are promised it will do — hardly a given, but stick with me — then we are left with a question: What does using it accomplish? If you do not have to pay attention or do work to meet your goal, does your goal mean anything at all? In some ways, that throws us back to some of the oldest and most profound questions our species has encountered: What does it mean to live a good life? How do we love others? What does it mean to love ourselves?

Which brings me back to my climbing buddy’s motivational poster. What is there to say about something he did not create? It expresses very little of him. It took only seconds and required no attention, devotion, or even thought. The main thing I took away was that he requires a reminder to do cool shit — which, at least in my view, hardly requires a reminder at all.

Once, I had some questions about why SpaceX, Elon Musk’s healthiest company, acquired xAI, his sickliest one. Now I have some questions about why we’re calling the whole thing SpaceX.

Look, what we have here, by revenue, is primarily a telecom company and a company that rents compute, according to SpaceX’s first quarterly earnings statement as a public company. The space sector of the business didn’t break a billion dollars this quarter and contributed only a touch over 10 percent of the company’s revenue. SpaceX remains its own biggest customer. There just aren’t enough other people who want its rockets. And, I dunno, maybe if the rockets were a primary focus of the company, SpaceX wouldn’t be in danger of blasting a new crater into the Moon with its space trash. 

The telecom part — SpaceX calls it “connectivity” — is Starlink, the satellite internet service, which had $4.2 billion in revenue, and was the only part of SpaceX that did not have a loss from operations. And while Gwynne Shotwell, on the call, outlined plans for a phone service meant to compete with AT&T, Verizon, and T-Mobile, the lion’s share of the spending (and the hype) is nonetheless around what is graciously being referred to as “AI” to capture whatever contributions Grok may be making. Analyst Alexander Potter expects spending on the neocloud business, which leases data center capacity to AI companies, to climb to $65 billion next year, $17 billion more than he’d previously estimated, Bloomberg notes.

It was hard to miss how important leasing data center space was to SpaceX. Not only did it make more money than SpaceX’s rockets, it also drove more spending — $15.8 billion on AI alone in the second quarter. (By comparison, spending on the space and connectivity sectors was a touch over a billion each.) That business makes it a competitor with neocloud companies such as CoreWeave and Nebius. 

Except that wasn’t originally the plan.

Musk built SpaceX’s Colossus 1 data center in Memphis for Grok, his also-ran AI that also occasionally refers to itself as MechaHitler and has a penchant for undressing women and children without their consent. But xAI had trouble running the complex, and decided to rent it out instead. In addition to encountering latency that made it hard to train in-house models, the center employed a mix of newer and older chips that created bottlenecks. On the earnings call, Musk said that only 10 percent of the compute SpaceX builds will go to Grok.

SpaceX now has deals with Google, Anthropic, Reflection AI, and Cursor, an AI company Musk eventually chose to acquire. On the second quarter earnings call, SpaceX chief financial officer Bret Johnsen said those deals put the company “on a trajectory, including contribution from Cursor, to reach $100 billion of ARR, or annualized revenue run rate,” a financial measurement used to estimate how much money a company will receive in a year, based on an estimate from a shorter period of time. Musk was even more bullish, saying that “the $100 billion ARR in December is not a question mark,” and that the actual ARR might be higher.

That’s very nice. But revenue isn’t profit, and building data centers is expensive. 

There are some problems with being in the bare-metal business of renting out compute — specifically, inevitable obsolescence, the vagaries of construction, and the fact that compute is basically a commodity, which means that companies compete on cost. The more data centers that get built, the more compute is available. With more compute available, companies can’t charge as much for their chips.

Musk has claimed that he took SpaceX public because he wanted to build data centers in space. Now, I might think that if I had trouble running a data center on the ground, I’d get better at it before I tried to do something an order of magnitude harder — something that had never been done before, even. But that is not how Musk thinks, I suppose. 

SpaceX has even proposed an orbital data center consisting of as many as 1 million satellites to the Federal Communications Commission. The application is light on technical details — we have no idea about satellite size or deployment schedule, for instance — which makes me think that it’s more for PR than anything else. Musk has also released a few details about the satellites — or, at least, some drawings of them. In this vision, a Musk-owned chip producer called Terafab will produce one terawatt of chips every year. A billion Optimus robots will do the work, at least once Musk figures out hands.

This all sounds expensive, doesn’t it? The end goal, Musk claims, is to build a mass accelerator on the Moon.

I don’t put much stock in this kind of thing, any more than I put much stock in Musk’s claims that Starlink will deliver “a majority of the world’s internet,” as he said on the earnings call. It’s all part and parcel of Musk’s general habit of talking about a sci-fi future as imagined by a bunch of mid-20th-century writers. Sure, there are a bunch of scientists suggesting Musk’s proposed space data centers are a bad idea. You can even argue about the feasibility of space data centers if you want. Remind me, did the Hyperloop ever get built? Say it with me now: It’s all vaporware till it ships, babe.

If we take away the pseudoscientific big talk, we are left with a company that launches rockets mostly for itself, has a fairly successful satellite internet business, and is basically running the risky and capital-intensive bare-metal business we’ve seen from a bunch of the neoclouds, as a result of its failures of its in-house AI. That’s not quite as dreamy as getting to Mars in six years, is it? 

What it does do, however, is provide Tesla with an important customer, one that buys $295 million in Tesla Megapack battery storage. SpaceX has also been buying a bunch of Cybertrucks, Musk’s historic flop of an automobile. As of this writing, Tesla stock is down 25 percent since January of this year.

Musk can maybe charge a premium on his compute because of his political connections — I don’t think he’s planning on splashing out $100 million on the midterms for nothing. Maybe those political connections will make it easier for Musk to build terrestrial data centers — though those seem to be wildly unpopular across the entire political spectrum. Who knows, maybe the game plan is just, That’s a nice AI business you have there. Would be a real shame if something happened to it. Perhaps you should buy my compute, so nothing happens.

But maybe I’m just thinking too long-term. After all, SpaceX’s insider lockups start expiring on August 6th, which is tomorrow. If those insiders sell — as short-sellers expect them to — the already slumping stock will be even worse off. So reminding everyone of the mass accelerator on the Moon is maybe meant to rally the troops. Good thing Nasdaq changed its rules for SpaceX, so those losses will hit everyone with an index fund!

Actually, I have a science fiction thought about that. Hey Elon! Why don’t you take a flying fuck at a rolling donut? Why don’t you take a flying fuck at the Mooooooon?