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There\u2019s a wild paradox in the middle of the biggest story in tech right now. The GPUs and other essential hardware that the hyperscalers are spending so lavishly to pack into their data centers with, it turns out, go obsolete in a hurry. That\u2019s the view detailed in an excellent new report from Research Affiliates, a firm that oversees around $200 billion in investment strategies for the RAFI index funds and ETFs. Author Chris Brightman\u2014he\u2019s RA\u2019s CEO\u2014contends that the AI arms race has effectively created a new industrial era. In this transformed ecosystem, companies aren\u2019t \u201cinvesting\u201d in the traditional sense. Rather, they\u2019re churning equipment at such an incredibly rapid tempo to generate sales that it\u2019s changing what is even meant by capex.<\/p>\n
\u201cThey\u2019re more like supermarkets than traditional tech or industrial enterprises, but their turnover isn\u2019t in the likes of grocery items. It\u2019s the stuff that generate their large language models, vector search and other products,\u201d Brightman told me in a phone interview. \u201cThey\u2019re in an arms race where they need to replace their hardware very rapidly, in other words, restock their shelves in a hurry.\u201d The problem, Brightman asserts, is that hyperscalers are taking losses on the large language models, vector databases and other products they\u2019re selling to companies and consumers, so the more hardware they buy, the more money they lose. \u201cRight now, each is using AI to maintain crucial dominance in their field, and that makes sense.\u201d Brightman observes. But, he adds, the immense spending needed to maintain those \u201cmoats\u201d and keep rivals at bay could generate puny returns going forward, and harm their overall profitability.<\/p>\n
In the article, Brightman spotlights the historic surge in AI capex that\u2019s mushroomed from $250 billion in 2024 to $650 billion this year by Bloomberg\u2019s estimate, equal to 2% of GDP. That industry\u2019s historic appetite for capital spawned the view that AI\u2019s becoming the new steel or railroads. But as Brightman points out, the equipment and infrastructure that supported those businesses is far different from the gear that drives AI. \u201cSteel mills and rail tracks depreciated over 40 to 45 years,\u201d he writes. He then contrasts those multi-decade useful lives to the scenario in AI. Hyperscalers such as Microsoft, Amazon, Alphabet and Meta are depreciating their GPUs and other hardware over roughly 5 or 6 years on their income statements. Although those spans appear short, he says, their real \u201clives\u201d are much shorter.<\/p>\n
In an economic sense, assets become fully depreciated, or turn obsolete, when the revenues they generate no longer cover their cost of acquisition (reflected in yearly depreciation), operating expense, and cost of capital. According to Brightman, the industry numbers show that AI hardware loses its value over about three years. As proof, he cites data on the profitability of Nvidia\u2019s industry-standard H100 GPUs. In their second year, a H100 spawned $36,000 in annual profit for a 137% return on investment. But by year four, the product was losing over $4,400 for a negative ROI of 34%, and the results sank fast from there. Writes Brightman, \u201cThe economic life of AI hardware is [a lot] shorter than its accounting life.\u201d<\/p>\n
It\u2019s not that the equipment wears out. Physically, it can actually run a lot longer. The reason AI hardware lose potency so fast: Nvidia, AMD and the other producers are crafting fresh offerings that each year provide enormous increases in computing power per watt deployed. Since the hyperscalers face tough energy constraints, they\u2019re constantly seeking gobs of new \u201ccompute\u201d using dollops of extra electricity. Normally, if typical manufacturers were adding capital at the pace the hyperscalers are setting in AI, they\u2019d already have built a gigantic base of equipment and infrastructure they could deploy for years, without the need to keep buying more. Not so in this brave new business. AI equipment is evolving so fast that each year, the hyperscalers need to replace an immense part of their capital base just to maintain the same capacity for forging AI wonders. \u201cMost of their spending isn\u2019t growth capex, it\u2019s \u2018maintenance\u2019 capex,\u201d says Brightman. Nevertheless, the overall numbers are so huge that although only about one-third goes to expansion, that\u2019s still good enough to hugely grow the volume of products and services they can deliver each year.<\/p>\n
The hyperscalers are using AI, and taking big losses, chiefly to protect their turf<\/p>\n
In our phone calls, Brightman nailed the conundrum for the giants of AI. \u201cAs they ramp the compute, they lose more and more money,\u201d he says. \u201cBut they have plenty of rationale to do so for now.\u201d All of the Big Four aim to provide the best AI features to enhance their signature offerings, and recognize that they\u2019ll lose their leadership in those staples if the AI component isn\u2019t top notch. Amazon makes most of its money providing computations and storage in the cloud. It\u2019s unable to recoup nearly the cost of the AI additions from its customers, says Brightman. \u201cBut it\u2019s sensible because if Amazon doesn\u2019t stay in the arms race, they\u2019ll lose the cloud business. They need the AI services as part of the cloud component.\u201d<\/p>\n
As for Microsoft, its staple is office software that generates subscription revenues, notably on its 360 platform. That franchise now faces stiff competition from Google\u2019s docs and sheets products. \u201cTo protect its existing business and keep its customers, Microsoft has to offer AI model services, even if it\u2019s losing money on its AI capex,\u201d declares Brightman. Alphabet is pre-eminent in \u201csearch,\u201d and cleans up as the world\u2019s biggest seller of online ads. Microsoft has mounted a challenge by launching its own search engine. \u201cTo continue its profitable line of business and keep its edge, Alphabet needs the AI element, and that requires big investments in data centers,\u201d says Brightman.<\/p>\n
Meta\u2019s got to worry about the other three invading its highly-lucrative, social media advertising business. \u201cPeople come to their platform to see the pictures and the video, and it costs Meta a lot of money to produce that content that supports the ads,\u201d notes Brightman. Meta uses AI to personalize feeds for users, rank content on instagram and Facebook, and check postings for safety, and needs those uses to maintain its lead. Yet once again, says Brightman, it can\u2019t yet charge enough for its ads to pay for its gigantic new spending needed to provide those fantastic features.<\/p>\n
Brightman concludes that the gusher in AI investment doesn\u2019t mean that this revolutionary advance will prove a big profit spinner for the Big Four. It\u2019s more a weapon for each titan to defend its domain. \u201cWhen capital turns over rapidly, and competition forces continuous reinvestment, extraordinary spending can sustain competitive position without creating value for shareholders,\u201d he states in the article. Once again, the shelf life of this what\u2019s filling our data centers is so brief that buying GPUs, say, is more like replenishing supermarket stocks than building a factories that endure for decades.<\/p>\n
On the other hand, Brightman told me that stuff that\u2019s costing these champions big time helped him greatly in preparing his analysis. \u201cA year ago, this project would have taken me nine months to do the research and modeling. But I used the best of Claude, ChatGPT, and Gemini, and synthesized their feedback, and did it start to finish in three weeks,\u201d he recounts. Brightman\u2019s vignette tells the story. This new industrial era may be a lot more beneficial to the folks and businesses that use the AI-enhanced products than the enterprises that furnish them.<\/p>\n
#dirty #secret #Big #Techs #arms #race #hardware #investments #obsolete #years<\/p>\n","protected":false},"excerpt":{"rendered":"
There\u2019s a wild paradox in the middle of the biggest story in tech right now….<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[245],"tags":[857,237,4291,483,5940,50,3858,1403,1021,8537,7429,8538,84],"_links":{"self":[{"href":"https:\/\/stock999.top\/index.php?rest_route=\/wp\/v2\/posts\/3916"}],"collection":[{"href":"https:\/\/stock999.top\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/stock999.top\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/stock999.top\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/stock999.top\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3916"}],"version-history":[{"count":0,"href":"https:\/\/stock999.top\/index.php?rest_route=\/wp\/v2\/posts\/3916\/revisions"}],"wp:attachment":[{"href":"https:\/\/stock999.top\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3916"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/stock999.top\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3916"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/stock999.top\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3916"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}