This Week in Charts: Prediction Market Accuracy, Software Valuations, AI Capex Concentration, and Call Center Labor Trends

This Week in Charts: Prediction Market Accuracy, Software Valuations, AI Capex Concentration, and Call Center Labor Trends An analysis of recent market data reveals that prediction markets show minimal partisan bias, software valuation multiples prioritize revenue growth over profitability-driven Rule of X scores, earnings revisions and capex are heavily concentrated in physical AI hardware over software, and global call center employment is contracting significantly. An NBER study by Prof. Zitzewitz analyzing over a century of prediction markets finds no statistically significant bias across political affiliation, gender, race, or age. The median Rule of X for public software rose from ~17% to ~23% over three years, but software multiples declined as investors favor revenue growth over profitability margin gains. Positive earnings revisions narrowed primarily to energy, hardware, and semiconductors, with ISM industrial backlogs hitting ~60% while broader non-hardware tech spending growth plateaued at ~30%. Revelio data shows global call center employment turned negative across all income tiers by Q4 2025, diverging from broader white-collar services. Prediction Markets and Partisan Bias After Kalshi odds anticipated Flávio Bolsonaro's first-round victory in Brazil weeks before equity markets fully reacted, analysis of an NBER working paper by Prof. Zitzewitz examined more than 100 years of prediction markets. The study determined that evidence of bias along lines of political affiliation, gender, race, or age is virtually non-existent, with return coefficients remaining statistically indistinguishable from zero. Divergence Between Rule of X and Software Multiples Public software companies improved median Rule of X performance from ~17% to ~23% over three years by replacing growth with profitability in response to interest rate regime changes. However, valuation multiples compressed simultaneously because markets value revenue growth over margin expansion, with companies exhibiting equivalent Rule of X scores clustering around 5–10x EV/NTM sales unless propelled by rapid revenue growth. AI Hardware Concentration and Narrowing Earnings Revisions Earnings revisions breadth has contracted, skewing heavily toward energy, hardware, and semiconductors, with profits increasingly expected to flow to physical infrastructure rather than software. While ISM industrial backlogs reached roughly 60% and Carlyle data indicates continued exponential growth in AI hardware shipments, broader enterprise IT spending growth has leveled off at approximately 30%. Global Contraction in Call Center Employment In contrast to minimal economy-wide evidence of an AI-driven jobs apocalypse, Revelio data reveals a sharp downturn in global call center employment. While lower-middle-income nations maintained growth longer than high-income peers, employment growth became negative across all income categories by Q4 2025, with call center headcount consistently lagging broader white-collar service roles outside China.

图表观察：预测市场偏见评估、科技股40法则失效与AI资本支出分化 本期通讯围绕多组核心数据展开分析：预测市场在选举预测中表现良好且统计上无明显党派偏见；软件领域传统的“40法则”由于过于依赖利润率提升而削弱了估值指示作用；市场盈利预期明显向能源与AI硬件等物理基础设施倾斜；同时，呼叫中心全球就业显现出AI替代带来的结构性下行趋势。 NBER工作论文显示预测市场在人口与政治党派维度上基本不存在统计显著的定价偏见。 上市软件公司40法则指标中位数上升，但由于主要是利润改善而非高增长驱动，估值倍数持续压缩并呈现负相关。 三季度及远期盈利预期上调高度集中于能源、半导体与硬件板块，软件与其它板块预期被下调。 制造业积压订单比例升至约60%，AI硬件支出呈指数增长，而泛科技支出增速已在约30%处平稳见顶。 受AI替代等因素影响，全球呼叫中心就业增长全面转负，低中等收入国家亦未能幸免。 预测市场表现与偏见检验 博索纳罗在巴西大选首轮胜选前，预测市场的赔率提前约三周转向，早于MSCI巴西ETF的大幅上涨。Zitzewitz教授在NBER发表的跨越百年的预测市场研究显示，无论在党派属性、性别、种族还是年龄维度，预测市场均未出现超过标准误差的系统性溢价或折价，表明预测市场在选举领域能较好排除党派偏见。 40法则对软件股估值的指示效力减弱 近三年上市软件公司的40法则中位数从约17%回升至约23%，但营收估值倍数却急剧下滑，两者滚动相关性转为负值。其核心原因在于企业以利润率取代了增速，而市场对营收增长赋予更高溢价；在20至40区间的企业估值普遍聚集在5至10倍EV/NTM销售额，仅高增长且具备行业顺风的企业能够享有估值溢价。 资本与盈利预期流向“原子”而非“比特” 分析师盈利向上修正的广度近期收窄，预期改善主要集中在能源和硬件/半导体领域，软件仅有微幅改善，其余行业多遭下调。ISM数据显示约60%的工业行业积压订单上升；凯雷投资组合数据显示AI硬件出货量仍呈指数级增长，而更广泛的IT支出增速则稳定在30%左右并停止加速，市场利益向底层基建聚拢。 呼叫中心就业显现AI冲击迹象 虽然整体就业市场尚未出现普遍的AI破坏，但Revelio数据显示全球呼叫中心就业增长已急剧转为负值。高收入国家自2022年即已转负，而此前维持增长的中低收入国家也在2025年第四季度全面转负，呼叫中心就业在除中国以外的主要非美市场明显落后于广泛白领服务业。

**We’re excited to announce that a16z is leading an investment in TypeSafe AI, the creators of Jev. Read more about why we invested [in our blog post](https://www.a16z.news/p/investing-in-typesafe) and hear from the team in our [a16z Show episode](https://www.youtube.com/watch?v=Ut3LOjKNJaE).** Earlier this week, Flávio Bolsonaro secured a surprise first-round victory in the Brazilian presidential election. Or maybe it wasn’t a surprise. Charts knows next to nothing about Brazilian politics, so we really couldn’t say. If you take a gander at the prediction markets, however, it appears as though Bolsonaro’s win was at least somewhat surprising, but not *too* surprising: After pacing as a long shot for most of the year, odds began to turn in Bolsonaro’s favor in August, and by the second week of September, he was already the favorite. Unsurprisingly, Bolsonaro’s odds for winning the presidency have since skyrocketed, after the recent first-round win. Whatever the surprise, prediction markets were early and right on this one by \~3 weeks. The outcome of the election is apparently consequential for financial markets too, judging by [MSCI’s Brazil ETF](https://www.bloomberg.com/quote/EWZ:US), which jumped \~15% after the news of Bolsonaro’s first-round win. To be fair, the ETF also started climbing back in August (when Bolsonaro’s Kalshi odds began to turn), but not nearly as steeply or as uniformly. In other words, it seems fair to conclude that prediction markets had this one first. All of this was a somewhat longwinded segue to some recent research that purports to show that prediction markets are actually pretty good at elections, or at least, they don’t demonstrate much partisan bias. Prof. Zitzewitz published some preliminary findings in an NBER working paper that aggregated over 100 years’ worth of prediction markets (albeit with most of the sample coming recently) looking for evidence of over-pricing along various dimensions, including demographics and political affiliation. The premise of the study is that any bias would show up as outsized returns (either positive or negative) for whichever attribute(s) prediction markets systematically over- or underrated. That makes sense: if, e.g., prediction markets consistently underrate female candidates, then taking the “over” on female candidates would be a winning strategy (reflected by a positive coefficient). As it turns out, the evidence of bias is pretty much non-existent: This chart summarizes a subset of the results, and among political affiliation, gender, color, or age, there is not a single dimension where the return coefficient was above or below zero by an amount that exceeded the standard error—the only exception is for non-US elections, where prediction markets appear to reflect some bias *in favor* of right-leaning candidates (i.e. the coefficient is negative because markets overrate their chances), albeit over a much smaller sample. Even there, however, the 95% confidence interval indicates that the bias is not statistically significant. Elsewhere, the results suggest a small (statistically insignificant) bias in favor of women and non-white candidates, and against older candidates, but when it comes to partisanship, the coefficient is a bare 0.03. Take that for what you will, but the results imply that when it comes to elections, prediction markets are pretty good at leaving their biases at the door. For years, the Rule of 40 was considered a lodestar for techco performance. The idea was to create a single metric that would enable comparisons across fast-growing companies at various stages of their lifecycle, where very rapid, and very unprofitable, growth was expected early, and less rapid, but more profitable, growth was expected as companies mature. The Rule of \[X\] part is determined by summing revenue growth with profit margins, and if the number was 40 or higher, you were in good shape. So a company with 100% revenue growth, and -60% margins, was still winning because 100 + (-60) = 40, i.e. Rule of 40 on the nose . . . whereas 150% growth with -150% margins, clocking in at Rule of 0, would be less impressive (despite the faster growth). Given that Rule of 40 is supposed to be a *good* thing, it’s odd to see these two things happening at once: public software cos are raising their Rule of X, while multiples are in steady decline: With some fits and starts, the median Rule of X for public software has risen from \~17% to \~23% over the past \~3 years, with the steepest upward inflection beginning early 2025 . . . just when revenue multiples began their precipitous decline. Here’s another way of looking at it: the correlation between Rule of X and multiples (on a rolling basis) turned sharply south and eventually negative: Again, it’s a weird thing. If Rule of X is a good thing, then an improving Rule of X should correspond to better multiples, and not the other way around. So, what’s going on? The most likely explanation for why Rule of X is weakening as a signal has to do with the component parts: growth and profitability. As laid out in [State of Markets](https://www.a16z.news/p/state-of-markets-ii), public (and private) techcos responded to post-ZIRP regime change by trading growth for profitability. What that means is that Rule of X is rising, but increased margins are doing most of the work, while growth makes a relatively smaller contribution. But when it comes to multiples, investors put more of a premium on the latter than the former, so even if the Rule of X number is high, if growth has slowed substantially, multiples compress accordingly. If you break techcos into their Rule of X buckets, you can see that revenue growth is just a much stronger factor than the combined Rule of X score: Mind the different scaling on both axes, but the general idea is pretty clear: higher growth companies tend to have higher multiples than lower growth companies with an equivalent Rule of X. In fact, for Rule of 20 all the way to Rule of 40, most companies are clustering in the same range between 5-10x EV/NTM sales—and it’s only the fastest growers (and really the ones with perceived tailwinds at their back, i.e. cyber and observability) that fetch a premium multiple. The point here is pretty straightforward: in this brave new world for techcos, Rule of 40 just isn’t as useful anymore as a performance benchmark. The SaaS-Prove-It regime is okay with profitability, but it really likes growth, and if you’ve got a lot of the former, but not much of the latter, then the market isn’t very impressed, Rule of X be damned. If you’ve got *both*, then that’s all the better, but there aren’t too many of those. Speaking of performance metrics where the headline picture may be obscuring some parts of what lies below, consider the recent downturn in the *breadth* of positive earnings revisions: When breadth is positive (as it’s been since July of ‘25), that means earnings revisions are relatively broad-based . . . and when it’s negative, it means the good news is limited to a narrower subset of companies. While revisions breadth is still positive, things have narrowed recently, with the implication that analyst optimism is training its fire, more so than before. And where, might you ask, does even-more-optimism still abound? It’s really only in two sectors: energy and tech, but more specifically hardware, not software: For the year, earnings revisions have been positive across the board (outside of utilities), but for Q3, the story is different: Energy improved, Tech improved—driven almost entirely by semis and hardware, with only the slightest uptick for software—and Financials improved modestly, as well, but every other sector experienced a downward revision from analysts. 2027 is a similar story: Energy, Hardware and Semis are the clear analyst winners, while the rest of the market, not so much. In other words, expectations are that profits will continue to flow to Atoms, substantially more so than Bits. AI capex shows no signs of slowing—if anything, the industrial backlog is increasingly broad-based: As per the ISM, the share of industrial industries reporting a higher order backlog is now \~60%, which is the highest it’s been since pandemania. At the same time as spending on the infra layer keeps piling up, there is some evidence that spending on everything else—while still high—is beginning to taper: According to Carlyle’s private portfolio company data, shipments of AI hardware are still climbing at their exponential pace, while the “other side of the AI spend coin,” i.e. growth of the broader category of tech-spend, has leveled off at \~30%. 30% growth is pretty high—and much higher than before—but growth does not appear to be accelerating any longer, which is again, not the case when it comes to hardware. The implication is that after a year of better than expected results for pretty much everyone, that upside surprise may be narrowing. Put another way, while the AI buildout keeps embiggering, that hasn’t changed expectations for how those benefits may flow through to the rest of the ecosystem—the epic run on atoms is picking up speed, even as it runs ahead of the field. Those unchanged-to-downwardly-revised forward estimates for everyone *outside* of AI infra are backed up by some hard data, as well, insofar as broader IT spending, while still growing, does not appear to be accelerating. Of course, the fact that analysts have made upward revisions to their estimates for most of the year means that they’ve gotten this “wrong” before, but for now, consensus is signaling that the AI buildout will stay in the driver’s seat, even more so than before. While the broader evidence of the [AI Jobs Apocalypse remains weak to non-existent](https://www.a16z.news/p/the-ai-job-apocalypse-is-a-complete)—and indeed, there’s evidence that AI has increased demand for technical and hard-hat roles—there is some data that shows at least one “AI exposed” category is taking it on the chin: call centers. According to data from Revelio, global call-center employment has gone sharply negative, after more than a decade of fairly consistent growth: To be fair, growth began slowing when interest rates went up, which is slightly before GPT was released into the wild . . . but the downtrend has steepened substantially since then, and has accelerated of late. Indeed, while lower-middle-income countries were relatively insulated from the downturn through most of 2024, even there growth has gone negative: While higher-income countries went negative in 2022, lower-middle income countries stayed positive for another \~2 years. But, as of Q4’25, call center employment growth has gone negative everywhere, even in the poorest countries. And lest one think that the downturn isn’t AI-driven (or specific to call centers), that too does not appear to be the case: Outside of China, interestingly enough, call center employment has lagged the broader category of white-collar service work in every major non-US market for call centers. Could there be other reasons for below-trend call center employment, besides AI? Probably, but call centers were always expected to be a likely target of substitution, so it seems reasonable to conclude that AI is the driver here. In the US, at least, the good news is that unemployment has been relatively stable, suggesting that the people who used to work in call centers appear to have found employment elsewhere . . . but changes are definitely underway, and one would expect more to come. *This newsletter is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. 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