Data Shows Strong AI Infrastructure Demand, Resilient Compute Spend, and Hiring Shifts Driven by Remote Work

Data Shows Strong AI Infrastructure Demand, Resilient Compute Spend, and Hiring Shifts Driven by Remote Work An analysis of public market indicators, manufacturing figures, labor statistics, and compute prices indicates that demand for AI infrastructure and services remains robust, with supply-chain bottlenecks and remote hiring playing larger roles than speculative demand collapses or AI-driven job losses. Vertiv missed revenue guidance despite reporting $3.27B in revenue, attributing the shortfall to supply chain congestion and complex project timelines. Manufacturing orders for power and cooling equipment like HVAC and turbines have nearly doubled compared to the prior decade, while computer and electronics unfilled-order backlogs remain around six months. Physical imports for data center power and cooling gear remain largely steady, though power conversion unit volumes dropped approximately 23% alongside a 25% price increase. Tech entry-level job postings have remained stable at about 5% of overall postings since 2019, while remote hiring arrangements have risen substantially, reaching over 45% in Information and Technical Services. Declines in Silicon Data's Token Cost Index reflect token-spend intensity shifts rather than declining demand, as B2B spend across OpenRouter models and GPU rental rates continue to rise. Data Center Infrastructure and Supply Chain Indicators Data center infrastructure supplier Vertiv posted $3.27B in Q2 revenue but missed expectations by around $76M, citing supply chain congestion and execution timing on larger projects. U.S. Census Bureau data shows manufacturing orders for data center and power-related machinery, particularly HVAC, have nearly doubled from the prior decade. While unfilled orders for computer and electronic products have inflected upward, the backlog ratio has stayed steady at just under six months since 2024. Although the NY Fed's Global Supply Chain Index shows pressure nearly two standard deviations above normal, import volumes for most data center cooling and power categories remain steady, with the exception of power conversion equipment, which saw a 23% decrease in physical units alongside a 25% price increase. Labor Market Dynamics and Remote Hiring Broader labor data challenges the narrative that AI is causing widespread job losses. In the tech sector, entry-level postings have held constant at roughly 5% of total job postings since 2019, suggesting that entry-level weakness is not a new AI-driven phenomenon; mid-level roles, however, appear more squeezed as senior shares rise. ADP figures indicate that remote work has expanded hiring beyond local labor pools, with the national long-distance hiring rate holding steady at 26.4%, about 30% above pre-pandemic levels. In Information and Technical Services, more than 45% of workers are now in long-distance arrangements, representing the highest share among tracked industries. AI Consumption Metrics and Compute Pricing While Silicon Data's Token Cost Index has trended downward since its May peak, the index tracks token-spend intensity—factoring in volume and cost—which can drop if adoption of lower-cost tokens outpaces high-cost tokens even as total usage grows. YipitData analysis of OpenRouter transactions demonstrates that combined B2B spending on Cursor, Anthropic, and OpenAI is rising both overall and at the median across software, business services, consumer, and financial sectors. Hardware pricing signals strong demand as well: 12-month contract pricing for Nvidia H100 GPUs reached just under $2.50 per GPU hour, representing a nearly 40% rise since November, while prediction market contracts on Kalshi price H100 rental rates at approximately $2.78 per hour.

数据透视AI基础设施需求、科技招聘现实与算力支出趋势 通过美国人口普查局、ADP、Silicon Data和YipitData等多方数据，本文分析了AI算力基建供应链瓶颈与订单激增现状、科技领域初级岗位占比低与远程招聘扩大的就业现实，以及企业端AI与GPU支出的持续上升态势。 Vertiv二季度营收达32.7亿美元，但因供应链拥堵等时差因素低于指引约7600万美元。 美国数据中心与电力设备订单较前十年水平接近翻倍，计算机与电子产品未完成订单积压时长稳定在近6个月的高位。 电力转换设备进口实物量较2025年1月下降约23%，但价格上涨约25%，其余冷却与电力硬件进口量基本保持稳定。 科技业初级岗位招聘长期仅占总招聘约5%，远距离招聘比例在信息技术服务领域高达45%以上。 企业在Cursor、Anthropic和OpenAI的支出持续走高，H100 GPU的12个月期合约价格升至近2.50美元/GPU时，较去年11月上涨近40%。 AI硬件基础设施：订单激增与供应链压力 数据中心冷却与电力基础设施需求高企。Vertiv营收虽大幅增长但因供应链拥堵未达指引。美国人口普查局数据显示，数据中心及电力机械订单较过去十年几近翻倍，特别是暖通空调（HVAC）设备需求极度火爆。未完成订单虽急剧攀升，但订单积压比（待交付订单除以月发货量）稳定在略低于6个月的水平，表明供需缺口虽大但产销仍在运转。与此同时，纽约联储的全球供应链压力指数上升近两个标准差。进口方面，除电力转换设备进口量下降23%、价格上涨25%外，其他关键类别的实物进口流量大体稳定，未见基础设施需求放缓迹象。 就业市场：初级岗位常态与远程招聘冲击 针对AI夺走就业机会的担忧，现有数据并未体现广泛的替代效应。科技行业的初级岗位招聘占比自2019年以来长期维持在5%左右，并未出现异常萎缩，反而中级员工的份额受到压缩。科技招聘的压力更多源于远程办公对本地人才的替代：ADP数据显示，全美远距离招聘比例稳定在26.4%，而信息技术服务领域有超过45%的雇员处于远距离雇佣关系，远高于疫情前水平，全球化人才库使得本地入门级雇员面临更大竞争。 AI消费指标：Token成本指数下行与算力支出高涨 Silicon Data的Token成本指数自5月高点后持续下滑，但这反映的是低成本Token消费增速超过高成本Token的消费结构变化，而非整体需求衰退。YipitData分析显示，软件、商业服务、消费以及金融行业在Cursor、Anthropic和OpenAI上的B2B支出总额和中位数均稳步增加。算力端同样保持强劲，GPU租赁价格普遍上涨，其中H100的12个月期合约价格升至近2.50美元/GPU时，较前一年11月攀升近40%，预测市场更押注其租金继续走高。

[America](https://www.a16z.news/t/america) | [Tech](https://www.a16z.news/t/technology) | [Opinion](https://www.a16z.news/t/opinion) | [Culture](https://www.a16z.news/t/culture) | [Charts](https://www.a16z.news/t/charts) There’s been a [delicate dance](https://www.a16z.news/i/203608554/ai-enabled-or-ai-enabled) in the public markets about who or what constitutes an AI winner and/or loser. To vastly oversimplify: *Oh, AI is going to render software obsolete . . . well maybe not everyone, and not yet, but eventually . . . but, in all events, all that AI disruption must be good for the business of serving AI . . . except that we really don’t like how much this all costs to build . . . but that’s at least good for the AI infra supply chain, which is where all the money is going . . . but, we’re also not sure it’s going to last, so . . .* And around and around it goes. Mr. Market doesn’t always make sense, because of course, Mr. Market doesn’t have to make sense, because there is no single Mr. Market. That’s true so far as it goes, and we offer no view of what the future Mr. Market will think, but we can at least offer a view of the present and past. In this case, returning to the question of “[yes, demand for AI infra is huge, but will it last,](https://www.a16z.news/i/208216895/are-semis-cheap)” one key player in the supply chain recently said something kind of interesting. Vertiv, a major supplier of cooling and power infrastructure for data centers—and a big winner of the buildout—added a substantial $3.27B in revenue for ‘Q2, but still undershot its revenue guidance by \~$76M (and undershot the street’s higher consensus guide by \~$120M). The company blamed the miss on “*minor timing shifts, primarily due to temporary supply chain congestion and multi-phased project execution as deployments scale in size and complexity*.” That sounds reasonable enough. Still, if Vertiv is having supply-chain issues, then presumably they’re not alone. And, if that’s the case, then does “supply chain congestion” materialize elsewhere in the data? The answer is “maybe yes, maybe no” but the more striking thing is simply visualizing (again) [how substantial the demand for “machines” has been.](https://www.a16z.news/i/207344025/data-centers-make-energy-cheaper) Census collects data on manufacturing orders, and the growth for some of these data center components is a thing to behold: Orders for Data Center and power-related machinery—HVAC, turbines, etc., and oil & gas—have nearly doubled from the previous decade levels. HVAC especially is just the hottest thing (and sits squarely in Vertiv’s wheelhouse), which makes sense, given all the heat that high energy compute generates. In terms of Vertiv’s supply-chain issues—or at least demand running ahead of supply—there too the data tells the story. Sort of. *Unfilled*-orders for computer and electronic products, which includes semiconductors, underwent a step-change \~2023, and inflected steeply upwards again, quite recently: “Unfilled-orders” is a proxy for a supply-demand imbalance (albeit an imperfect one) for the obvious reason that orders tend to go unfilled when demand exceeds supply. It doesn’t necessarily tell you, though, if there’s a supply-chain issue, or if demand is simply more than what supply can handle. A somewhat better proxy for supply-demand v. supply-chain is the “backlog ratio,” which divides the backlog by monthly shipments. In that case, a bigger backlog balanced against even more shipments, would imply that demand is simply continuing to grow ahead of growing supply, but that supply chains are working just fine. In this case, the backlog ratio looks pretty stable—definitely high, but stable. At just under 6 months (i.e. the time it would take at current volumes to fill unmet orders), the backlog is very high relative to prior, but about the same as it’s been since 2024. So, no supply-chain issues there, but for comparison’s sake, the NY Fed’s Global Supply Chain Index (also on the chart) has most definitely inflected upwards, such that “supply chain pressure” is nearly two standard deviations higher than usual. So, clearly there are some global supply chain issues, but whether those are impacting data center machinery specifically, it’s hard to say. The truth is that even if the supply chain disruptions did impact machinery imports, that could have the effect of slowing new orders, which would keep the import ratio in balance, anyway. Again, the data is inconclusive. One last thing to look at: actual international import data. Census counts the stuff moving in and out of the country, so we can see if there’s been any change—perhaps a sudden drop in physical imports: No dice. For the categories of imports closest to what data centers need for power and cooling, once again, the story looks pretty stable. Other than “power conversion,” where physical units are \~23% lower than they were back in Jan. ‘25 (while prices are \~25% higher), every other category of import is about as numerous as it’s been for the past year and a half. *Prices* have gone up substantially, but the physical flow of goods is mostly unchanged. So, perhaps converter machinery has become snaggled in the supply chain, but for the other categories, the picture looks ‘business-as-usual.’ Of course, the counterfactual of how much higher imports might have been without a supply-chain disruption is unknowable from this data alone, so we’ll just have to take Vertiv (and the NY Fed) at their respective word. What does it all mean? The same thing it’s meant for a while now: demand for (and imports of) AI infrastructure has been historically high, and well-ahead of what supply can timely meet. Maybe supply-chain disruptions have made it worse, maybe not—[certainly dependency on Chinese imports has been a concern](https://www.bloomberg.com/news/features/2026-04-01/us-ai-data-center-expansion-relies-on-chinese-electrical-equipment-imports), as has the potential impact of a production backlog on timely data center development, but that’s been true for a while. Bigger picture, though, if demand for AI infra has slowed, it’s certainly not showing up in this data. We’ve pointed out previously that when it comes to [the “AI Took My Job” story, the data simply does not support the claim](https://www.a16z.news/p/the-ai-job-apocalypse-is-a-complete), and if anything, [points the other direction](https://www.a16z.news/i/207344025/entry-level-tailwinds). While it’s still too early to know the true labor impact effects, for now at least, the evidence suggests that AI has been more a jobmaker than jobtaker. That said, there has been one persistent weak spot in the job market, and [that’s at the entry-level](https://www.a16z.news/i/191479972/1-its-a-tough-job-market-for-young-people-knowledge-worker-or-otherwise). It’s hard for young folks to get hired. There too, however, there’s [some evidence that AI is making, rather than taking entry-level jobs.](https://www.a16z.news/i/207344025/entry-level-tailwinds) But that’s all stuff that we’ve covered before. This week’s observation is that it turns out that entry-level jobs have *always* been a fairly small part of the tech hiring pie, and that hasn’t changed much, at all: Not that it’s much consolation to the people struggling to get hired, but entry-level job postings in Tech are roughly \~5% of overall postings, and it’s been that way since 2019. If anything, *mid-level hires* are getting squeezed, while senior hires increase their share. In fact, if there’s any sector to blame for less entry-level hiring it’s healthcare, where the entry-level share is substantial, but falling since 2023. When it comes to tech, though, a more plausible explanation for entry- or perhaps mid-level hiring malaise is that [workers are working longer,](https://www.a16z.news/i/191479972/1-its-a-tough-job-market-for-young-people-knowledge-worker-or-otherwise) and that [WFH/Remote Hiring has unlocked a global talent pool](https://www.a16z.news/i/200478177/ai-remote-work-took-my-job) for tech to hire (creating less of an impetus to hire relatively inexperienced local options). According to data from ADP, the “long distance hire rate” jumped during the pandemic and has held steady at 26.4%, or \~30% higher than before: Despite all the RTO mandates, hiring remotely has been a tough habit to kick. And which sector has increased its share of “long distance arrangements” the most? It’s tech: Again, according to ADP, Information (and Technical Services) have 45%+ of workers in some kind of long distance relationship—more than any other sector, and substantially more than before the pandemic. That number sounds too high to believe, and maybe so, but the direction passes the smell-test. AI probably didn’t take your job, but WFH certainly may have. With all the chatter around open weight model competition, token pricing, and demand, it’s always instructive to take a quick peek at some actual, albeit imperfect, [indicators of AI spend](https://www.a16z.news/i/207344025/ai-demand-growth-ai-spend-growth). The first indicator has spread like nightmare fuel, but what it actually represents is a good deal more nuanced: Silicon Data’s Token Cost Index has been in mostly serial decline since its May peak. *AI Demand Is Collapsing! Run for the Hills!* Well, no, not necessarily. As per SiliconData, the index measures token-spend *intensity*. It’s a combination of how many tokens are being consumed and at what cost. To oversimplify somewhat, if demand for high-cost tokens increases, but demand for lower-cost tokens increases even more, then the index will fall—even as overall demand increases. Is that what is happening right now? It’s hard to say. But other data suggests that that may very well be the case (and that otherwise demand and spending are both alive and well). As per YipitData’s analysis of OpenRouter data, the combined B2B spend on Cursor, Anthropic and OAI is increasing both in total and at the median (for the four biggest spending industries): Unsurprisingly, software is by far the biggest spender, but business services, consumer and especially financials, are all trending up and to the right. That financial services would be ripe for AI adoption has been conventional wisdom all along, and there’s plenty of other evidence that banks, investment management and other financials are investing heavily in AI. Likewise, also from Silicon Data, both spot GPU rental rates, and longer-term contract pricing continue to climb: With the exception of A100 spot-pricing (which is pretty stable), renting a GPU continues to get a little more expensive—and not just for the latest edition. As for longer-term agreements, arguably a better indicator of demand, the term-rate curve for the H100—the second oldest GPU in the set—has moved progressively higher in recent weeks (and is much higher than it was in November of last year): 12-month pricing for the H100, according to Silicon Data, sits just under $2.50/GPUhr—almost 40% higher than it was in November of last year. For what it’s worth, and this data is from earlier in the week, prediction markets think the H100 is only getting pricier: Kalshi markets put the rental rate at \~$2.78/hr, or about 5 cents higher than what it was when the data was collected. Make of this what you will, but this data at least suggests that both demand and spending continue to cook. *This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. Furthermore, this content is not investment advice, nor is it intended for use by any investors or prospective investors in any a16z funds. This newsletter may link to other websites or contain other information obtained from third-party sources - a16z has not independently verified nor makes any representations about the current or enduring accuracy of such information. If this content includes third-party advertisements, a16z has not reviewed such advertisements and does not endorse any advertising content or related companies contained therein. 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