🕷️ Crawler Inspector

URL Lookup

Direct Parameter Lookup

Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 36 (from laksa162)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa036.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://cepr.org/voxeu/columns/unpacking-us-tech-valuations-agnostic-assessment\')), getAhrefsUnparsedNoserviceFromURL(\'https://cepr.org/voxeu/columns/unpacking-us-tech-valuations-agnostic-assessment\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/cepr.org\/voxeu\/columns\/unpacking-us-tech-valuations-agnostic-assessment","crawl_time":1775275564,"first_indexed_time":1757286780,"http_code":200,"src_unparsed":"org,cepr!\/voxeu\/columns\/unpacking-us-tech-valuations-agnostic-assessment s443","src_root_hash":"12878714870991386236","history_drop_reason":null,"meta_title":"Unpacking US tech valuations: An agnostic assessment | CEPR","meta_descriptions":["The recent performance of the largest US tech companies has raised concerns about the risk of a stock market bubble. Using a three-stage pricing model applied to the top ten US tech firms, this column finds that current market prices do not appear implausible given the underlying drivers of both recent and expected performance. The adoption of AI technologies will be a major engine of growth, which is already transforming markets and displacing jobs. However, risks will remain around the ability to identify reliable AI applications and the growing competition from abroad."],"attrs_boilerpipe_text":"Innovations in artificial intelligence (AI) have fuelled substantial stock price gains for listed US technology companies. Nvidia, the most focused on AI technology, has been the first company to reach a $4 trillion capitalisation at the beginning of July, followed by Microsoft three weeks later. The top ten US tech companies\n1\n (henceforth, Mag10) now account for one-third and one-sixth of total US and global stock market capitalisations, respectively (Figure 1).\n2\nAlongside rising concentration, the elevated and growing valuations of US companies (as measured by their price\/earnings ratios; Figure 2) have been a top-of-mind concern for financial analysts, prompting them to debate whether the US stock market is currently experiencing a bubble like the dot-com era of the late 1990s (Marks 2025, Stacey and Novik 2025, Duguid et al. 2025).\nIn this column, we contribute to the debate by first presenting descriptive evidence on the valuations and fundamentals of the largest US tech companies. We then apply a simple three-stage pricing model to derive their market-implied expected profit growth rates. Finally, we discuss the plausibility of such expected growth rates.\nDescriptive evidence\nPrice\/earnings (P\/E) ratios for the US stock market have been on an increasing trajectory for more than a decade, with a notable surge during the Covid period, and are indeed approaching the levels observed during the dot-com era (Figure 2). In contrast, t\nhe P\/E ratios of the largest tech companies remain well below the dot-com extremes, also because stock prices have not skyrocketed as spectacularly as they did in the late 1990s\n(Figures 3 and 4).\nBy focusing on the Mag10, we find significant heterogeneity in their current and forward P\/E ratios\n(between 20 and more than 600; Table 1). The smaller companies (Broadcom, Oracle, Palantir, and Tesla) are those that have the higher multiples. These are also the companies that have recently made – or are in the process of making – inroads into new, mostly AI-related businesses that have high growth potential and are expected to significantly change the level and composition of their earnings.\n3\nThe larger companies, instead, have much lower multiples. This may reflect investors’ expectations that their profits are unlikely to experience spectacular growth rates, given their already huge size and reliance – at least in part – on fairly traditional and saturated markets. Finally, Google’s relatively low P\/E ratio (around 20) seems to indicate that investors are not indiscriminately pouring money into AI companies: while Google has developed frontier and, in some cases, best-in-class AI models, it is not yet clear that its AI strategy is able to counter threats to its most lucrative business (Search), which might be displaced by the rapid change in users’ behaviour (LLM use instead of Internet navigation). \nTable 1\nMag10 market capitalisation and price\/earnings multiples\nSource\n: LSEG.\nNote\n: Average values for 1-13 August 2025.\nThis preliminary descriptive evidence suggests that\ninvestors are still significantly discriminating tech and AI-related firms by their idiosyncratic earnings prospects. Such discrimination tends to vanish during bubble episodes\nand other periods when sentiment and herd behaviour substantially shape valuations (Baker and Wurgler 2006, Chang et al. 2000).\nEvidence from an equilibrium dividend discount model\nA stock’s price should be equal to the present discounted value of its future dividends. Elevated P\/E ratios may signal optimism about future profit and dividend growth, optimism that could prove either well-founded or misplaced, thereby exposing the market to the risk of abrupt corrections. In Albori et al. (forthcoming), we use an equilibrium dividend discount model (Molodovsky et al. 1965, Fuller and Hsia 1984, Sorensen and Williamson 1985)\nto compute the abnormal growth rates of earnings that would justify current equity valuations, that is, make them compatible with rational pricing in line with historical norms\n. Table 2 shows the model-implied growth rates, together with recent earnings growth rates and analysts’ expected growth rates, all inflation-adjusted.\nTable 2\nMag10 recent, expected, and model-implied growth in earnings per share (percent)\nSource\n: LSEG, our estimates.\nNote\n: Real (inflation-adjusted) annual growth rates in earnings per share (EPS). For Tesla, historical EPS growth is over the past four years; for Palantir, the past year. Growth expected by I\/B\/E\/S analysts refers to the average growth rate over a period of three to five years. Model-implied growth rates are derived from P\/E ratios as recorded on 28 July 2025.\nFor the six largest companies in our sample, the average model-implied annual growth rate is 12%\n, with moderate dispersion.\nThis indicates a significant deceleration from the 33% average growth observed over the past five years\nand is also more conservative than the 15% growth expected by analysts.\nFor the remaining four companies, the mean model-implied growth rate is 41%\n, which compares with 39% in the previous five years and 17% expected by analysts. As discussed in the previous section, these four companies are radically different from the other six, in that they are much smaller and have recently entered fast-growing AI-related markets.\nTheir implied growth rates are compatible with scenarios – far from being guaranteed, but also not utterly unrealistic – in which they manage to scale up their new business lines at a pace similar to that kept in recent years.\nIn summary, current equity valuations for the top US tech companies are rational if one expects high, but not historically unusual growth rates for the coming years. Apart from historical comparisons, determining if these expected growth rates are realistic is challenging. This assessment relies heavily on subjective judgments about market developments, particularly the future demand for AI-related services and improvements in productivity and profitability driven by AI adoption. In the following section, we present information that may help inform these judgments. \nFactors affecting future earnings growth\nThe main factors that contributed to the spectacular earnings growth of US tech companies in recent years (Figures 5 and 6) were:\nStrong increases in global spending on digital advertisements\n(by 15% annually on average between 2019 and 2024; Kemp 2025), often driven by the displacement of traditional advertisements (e.g. on TV and newspapers) and by the rise of e-commerce at the expense of brick-and-mortar stores. Amazon, Google, and Meta are market leaders in this space and have continued to expand their market shares (in 2024, they accounted for 51% of global ad sales; BestMediaInfo Bureau 2025).\nIncreased corporate spending on both traditional and AI-related cloud services\n(up by 21% annually on average between 2019 and 2024; Slingerland 2025). Amazon, Google, and Microsoft are the main providers of these services, with a combined global market share of 63% (Brindley and Zhang 2025). Significant fractions of the hardware bought by these players to scale up their data centres is provided by NVIDIA and Broadcom.\nStill rapid global expansion of the number of smartphones\n(+5% average yearly growth between 2019 and 2024), coupled with the continued increase in the amount of time spent by users in the interaction with these devices (Howarth 2025). This has sustained the sales of phones (Apple, Google) and consumer-oriented cloud services (Apple, Google, Microsoft), and has helped fuel the previous two trends (spending on digital ads and cloud infrastructure).\nGains in efficiency driven by economies of scale and AI adoption.\nFor example, software development represents a significant fraction of the cost of offering the products and services mentioned above, but its cost tends to increase less than proportionally as the user base expands (the same piece of software serves more users). Moreover, AI has already facilitated the automation of labour-intensive tasks, such as content-moderation on social platforms (Meta). Reportedly, AI has also improved the economics of digital advertising, by allowing for a better targeting of users.\nAccording to the latest quarterly reports of the Mag10 (which on average showed still sustained growth), as well as to the expert sources cited above,\nnone of these trends is expected to end abruptly in the near future, although some of them are expected to slow down.\nFor example, smartphone adoption is expected to be sustained mostly by emerging markets, as advanced ones have already been saturated (Bellan 2025). Similarly, future growth in digital-advertisement sales may be hindered by the already large market shares held by the Mag10, although also in this case emerging markets represent a significant expansion opportunity.\nThe growth prospects of cloud services (and related hardware) are probably the most controversial ones.\nWhile some analysts expect AI adoption to keep propping up cloud spending for the foreseeable future (Goldman Sachs 2024), others deem that current AI spending by corporations is bubbly and likely to slow down, as the economics of AI applications are often unproven (Widder and Hicks 2025). Whatever the stance on this issue, most analysts seem to agree that AI spending is the key factor that will determine whether tech companies are able to achieve the high growth rates envisaged by financial analysts and embedded in stock prices (e.g. our model-implied estimates).\nWhile taking a position in this debate is complex, we note that\nsome recent trends seem to support the view that the demand for AI services will keep increasing at a brisk pace:\nAs noted in a recent Economist article, the use of large language models (LLMs), such as ChatGPT, is quickly displacing Internet navigation (15% search traffic drop in the past year), as users find it more convenient to receive responses to their queries from an LLM rather than sifting through multiple websites (The Economist 2025a). This trend has just started, and it is likely to continue. The consequence is that a large portion of the revenues and profits previously made by millions of web-site owners and content creators will be shifted to cloud-service providers and hardware vendors (Godoy 2025, Weiss 2024), as on-the-fly content creation by LLMs requires enormous amounts of computational resources. At present, some of the major AI players still report difficulties in meeting these computational requirements, despite the huge investments in infrastructure made in previous years.\nJob destruction by generative AI technologies (not only LLMs, but also image and video generators) is reportedly gaining traction and economic significance not only in the website-ownership space, but also in adjacent occupations (ad creatives, illustrators, writers and copywriters, photographers, models, translators, moderators, etc.) (Bartholomew 2025). For example, according to a 2024 survey conducted by the Society of Authors (SOA Policy Team (2024), 26% of illustrators and 36% of translators report having already lost work due to generative AI, and three-quarters of them expect future income reductions. As explained in the previous point, this kind of displacement creates demand for the hardware and cloud services sold by the Mag10.\nAI technology is being increasingly adopted in the automotive sector (not only in autonomous vehicles, but also for driving assistance and infotainment). This trend – which will also significantly prop up the demand for cloud services, given the huge size of the automotive sector – has begun only recently, and it seems unlikely to abate, given that most auto-industry executives expect vehicles to become increasingly ‘software-defined’ (IBM 2025).\nThese are just some examples of large markets where substantial AI adoption and, in some cases, the consequent displacement of jobs are not just a future possibility, but something that is already happening at scale. Mechanically, this adoption generates demand for compute, which is mostly offered by large US tech firms.\nConclusions\nThe largest US technology companies have recently recorded substantial increases in their valuations, prompting financial analysts and policymakers to express concerns about the risk of a stock market bubble. In this column, we present model-based evidence suggesting that the earnings growth rates required to justify current prices do not appear implausible given the underlying drivers of recent and expected performance. We also argue that the adoption of generative AI technologies will be a major engine of growth, and it is already transforming markets and displacing jobs at scale. Its rollout fuels rising demand for computing power, largely supplied by US tech companies, reinforcing their dominance. However, some cautions are in order. Some industries still struggle to identify reliable applications, sparking short-term disillusionment. This could temporarily curb corporate AI spending and drive a re-pricing of technology stocks (The Economist 2025b). Added to this, intensifying competition, particularly from China, threatens to erode big tech’s profit margins (Mims 2025). These factors could make the realisation of the growth rates implied by our model less certain.\nAuthors’ note: The opinions expressed in this column are those of the authors and do not necessarily reflect the views of the Bank of Italy.\nReferences\nAlbori, M, V Nispi Landi and M Taboga (forthcoming), “Is there a bubble in the US stock market? Evidence from an agnostic valuation procedure”, Bank of Italy Occasional Papers.\nBaker, M and J Wurgler (2006), “Investor sentiment and the cross‐section of stock returns”,\nThe Journal of Finance\n61: 1645-1680.\nBartholomew, J (2025), “It’s happening fast’ – creative workers and professionals share their fears and hopes about the rise of AI”,\nThe Guardian.\nBellan, R (2025), “Apple: Pay attention to emerging markets, not falling China sales”, TechCrunch.\nBestMediaInfo Bureau (2025), “Google, Meta, Amazon’s combined share of global ad revenues hits 51% in 2024: Magna”.\nBrindley, R and Y Zhang (2025), “Worldwide cloud service spending to grow by 19% in 2025”, Canalys.\nChang, E C, J W Cheng and A Khorana (2000), “An examination of herd behavior in equity markets: An international perspective”,\nJournal of Banking & Finance\n24: 1651-1679.\nDuguid, K, G Steer, H Clarfelt and I Smith (2025), “Wall Street ‘euphoria’ sparks bubble warnings”,\nFinancial Times.\nFuller, R J and C C Hsia (1984), “A simplified common stock valuation model”,\nFinancial Analysts Journal\n40: 49-56.\nGodoy, J (2025), “Google's AI previews erode the internet, US edtech company says in lawsuit”, Reuters.\nGoldman Sachs (2024), “Cloud revenues poised to reach $2 trillion by 2030 amid AI rollout”, GS Artificial Intelligence.\nHowarth, J (2025), “How Many People Own Smartphones?”, Exploding Topics.\nIBM (2025), “Automotive in the AI era”, IBM Institute for Business Value.\nKemp, S (2025), “Digital 2025: Global advertising trends”, DataReportal.\nMarks, H (2025), “Is the US stock market in bubble territory?”,\nFinancial Times\n.\nMims, C (2025), “Silicon Valley’s New Strategy: Move Slow and Build Things”, The Wall Street Journal.\nMolodovsky, N, C May and S Chottiner (1965), “Common stock valuation: Principles, tables and application”,\nFinancial Analysts Journal\n21: 104–123.\nSlingerland, C (2025), “90+ Cloud Computing Statistics: A 2025 Market Snapshot”, CloudZero.\nSOA Policy Team (2024), “SoA survey reveals a third of translators and quarter of illustrators losing work to AI”, The Society of Authors.\nSorensen, E H and D A Williamson (1985), “Some evidence on the value of dividend discount models”,\nFinancial Analysts Journal\n4: 60-69.\nStacey, S and M Novik (2025), “How big is the stock market’s America bubble?”,\nFinancial Times.\nThe Economist\n(2025a), “AI is killing the web. Can anything save it?”.\nThe Economist\n(2025b), “Welcome to the AI trough of disillusionment”.\nWeiss, T (2025), “The Decline of Stack Overflow”, Data Science with R.\nWidder, D G and M Hicks (2025), “Watching the Generative AI Hype Bubble Deflate”, Digital Life Initiative, Cornell University.","attrs_markdown":"[Skip to main content](https:\/\/cepr.org\/voxeu\/columns\/unpacking-us-tech-valuations-agnostic-assessment#main-content)\n\nMobile menu\n\nSearch\n\n- [VoxEU](https:\/\/cepr.org\/voxeu)\n  - [Search All Columns](https:\/\/cepr.org\/voxeu\/search-all-columns)\n  - [VoxTalks](https:\/\/cepr.org\/voxeu\/voxtalks)\n  - [VideoVox](https:\/\/cepr.org\/audio-and-video\/voxeu-videos)\n  - [About VoxEU](https:\/\/cepr.org\/voxeu\/about-voxeu)\n- [Research](https:\/\/cepr.org\/research)\n  - [Programme Areas](https:\/\/cepr.org\/research\/programme-areas)\n  - [Research Policy Networks (RPNs)](https:\/\/cepr.org\/research\/research-policy-networks-rpn)\n  - [Research Projects and Networks](https:\/\/cepr.org\/research\/research-projects-and-networks)\n  - [Research Policies](https:\/\/cepr.org\/research\/research-policies)\n  - [Data Sets](https:\/\/cepr.org\/research\/data-set-items)\n- [Publications](https:\/\/cepr.org\/publications)\n  - [Publication Series](https:\/\/cepr.org\/publications\/publication-series)\n  - [Books and Reports](https:\/\/cepr.org\/publications\/books-and-reports)\n  - [Discussion Papers](https:\/\/cepr.org\/publications\/discussion-papers)\n  - [Covid Economics Papers](https:\/\/cepr.org\/publications\/covid-economics-papers)\n  - [Policy Papers](https:\/\/cepr.org\/publications\/policy-insights-policy-papers)\n  - [Economic Policy Journal](https:\/\/cepr.org\/publications\/economic-policy-journal)\n  - [Search All Publications](https:\/\/cepr.org\/search?content_type%5B14%5D=14&type=publication)\n  - [Archive](https:\/\/cepr.org\/publications\/archive)\n- [Events](https:\/\/cepr.org\/events)\n  - [Calls for Papers](https:\/\/cepr.org\/events\/open-calls-papers)\n  - [Forthcoming Events](https:\/\/cepr.org\/events\/forthcoming-events)\n  - [Past Events](https:\/\/cepr.org\/events\/past-events)\n  - [Event Series](https:\/\/cepr.org\/events\/event-series)\n- [Audio and video](https:\/\/cepr.org\/audio-and-video)\n  - [Search all multimedia content](https:\/\/cepr.org\/audio-and-video\/search-all-multimedia)\n  - [VoxTalks](https:\/\/cepr.org\/voxeu\/voxtalks)\n  - [VideoVox](https:\/\/cepr.org\/audio-and-video\/voxeu-videos)\n  - [Vox Webcasts](https:\/\/cepr.org\/voxeu\/vox-webcasts)\n  - [Webinar Videos](https:\/\/cepr.org\/audio-and-video\/webinar-videos)\n- [About CEPR](https:\/\/cepr.org\/about)\n  - [People](https:\/\/cepr.org\/about\/people)\n  - [News](https:\/\/cepr.org\/about\/news)\n  - [Advisory Board](https:\/\/cepr.org\/about\/people\/advisory-board)\n  - [Press](https:\/\/cepr.org\/about\/press)\n  - [Governance](https:\/\/cepr.org\/about\/governance)\n  - [Support CEPR](https:\/\/cepr.org\/about\/join-cepr)\n  - [Partners](https:\/\/cepr.org\/partners)\n  - [Jobs at CEPR](https:\/\/cepr.org\/about\/jobs-at-cepr)\n\nThemes\n\nSearch\n\nThemes\n\n## Themes\n- Key Themes\n- [Artificial Intelligence](https:\/\/cepr.org\/themes\/artificial-intelligence)\n- [Central Banking](https:\/\/cepr.org\/themes\/central-banking)\n- [Climate Change](https:\/\/cepr.org\/themes\/climate-change)\n- [Competition Policy](https:\/\/cepr.org\/themes\/competition-policy)\n- [Development & Growth](https:\/\/cepr.org\/themes\/development-growth)\n- [Economic History](https:\/\/cepr.org\/themes\/economic-history)\n- [Energy](https:\/\/cepr.org\/themes\/energy)\n- [EU Economic Architecture](https:\/\/cepr.org\/themes\/eu-economic-architecture \"EU Economic Architecture\")\n- [Inflation](https:\/\/cepr.org\/themes\/inflation)\n- [Political Economy](https:\/\/cepr.org\/themes\/political-economy \"Political Economy\")\n- [Poverty and income inequality](https:\/\/cepr.org\/themes\/poverty-and-income-inequality \"Poverty and income inequality\")\n- [Sustainable Finance](https:\/\/cepr.org\/themes\/sustainable-finance)\n- [Ukraine Initiative](https:\/\/cepr.org\/themes\/ukraine-initiative)\n- [Women in Economics](https:\/\/cepr.org\/themes\/women-economics)\n- Other Themes\n- [Business Cycles](https:\/\/cepr.org\/themes\/business-cycles)\n- [Covid 19](https:\/\/cepr.org\/themes\/covid-19)\n- [Exchange Rates](https:\/\/cepr.org\/themes\/exchange-rates)\n- [Finance & Fintech](https:\/\/cepr.org\/themes\/finance-and-fintech \"Finance & Fintech\")\n- [Health Economics](https:\/\/cepr.org\/themes\/health-economics)\n- [International Finance](https:\/\/cepr.org\/themes\/international-finance \"International Finance\")\n- [International Trade](https:\/\/cepr.org\/themes\/international-trade \"International Trade\")\n- [Labour Markets](https:\/\/cepr.org\/themes\/labour-markets)\n- [Migration](https:\/\/cepr.org\/themes\/migration \"Migration\")\n- [Monetary Policy](https:\/\/cepr.org\/themes\/monetary-policy \"Monetary Policy\")\n- [Productivity and Innovation](https:\/\/cepr.org\/themes\/productivity-and-innovation \"Productivity and Innovation\")\n- [Taxation](https:\/\/cepr.org\/themes\/taxation \"Taxation\")\n- Focus On\n- [The Economic Consequences of the War](https:\/\/cepr.org\/debates\/economic-consequences-war)\n- [Europe-Asia Initiative](https:\/\/cepr.org\/europe-asia-initiative)\n- [Trump & Tariffs](https:\/\/cepr.org\/voxeu\/trump-and-tariffs)\n\n## Main navigation\n- [VoxEU](https:\/\/cepr.org\/voxeu)\n  - [Search All Columns](https:\/\/cepr.org\/voxeu\/search-all-columns)\n  - [VoxTalks](https:\/\/cepr.org\/voxeu\/voxtalks)\n  - [VideoVox](https:\/\/cepr.org\/audio-and-video\/voxeu-videos)\n  - [About VoxEU](https:\/\/cepr.org\/voxeu\/about-voxeu)\n- [Research](https:\/\/cepr.org\/research)\n- [Publications](https:\/\/cepr.org\/publications)\n  - [Publication Series](https:\/\/cepr.org\/publications\/publication-series)\n  - [Books and Reports](https:\/\/cepr.org\/publications\/books-and-reports)\n  - [Discussion Papers](https:\/\/cepr.org\/publications\/discussion-papers)\n    - [About CEPR Discussion Papers](https:\/\/cepr.org\/publications\/discussion-papers\/about-cepr-discussion-papers)\n    - [Discussion Paper Subscriptions](https:\/\/cepr.org\/publications\/discussion-papers\/discussion-paper-subscriptions)\n    - [Search Discussion Papers](https:\/\/cepr.org\/publications\/discussion-papers\/search-discussion-papers)\n  - [Covid Economics Papers](https:\/\/cepr.org\/publications\/covid-economics-papers)\n  - [Policy Papers](https:\/\/cepr.org\/publications\/policy-insights-policy-papers)\n  - [Economic Policy Journal](https:\/\/cepr.org\/publications\/economic-policy-journal)\n  - [Search All Publications](https:\/\/cepr.org\/search?content_type%5B14%5D=14&type=publication)\n  - [Archive](https:\/\/cepr.org\/publications\/archive)\n    - [CEPR Bulletin](https:\/\/cepr.org\/publications\/archive\/cepr-bulletin)\n- [Events](https:\/\/cepr.org\/events)\n- [Audio and video](https:\/\/cepr.org\/audio-and-video)\n  - [Search all multimedia content](https:\/\/cepr.org\/audio-and-video\/search-all-multimedia)\n  - [VoxTalks](https:\/\/cepr.org\/voxeu\/voxtalks)\n  - [VideoVox](https:\/\/cepr.org\/audio-and-video\/voxeu-videos)\n  - [Vox Webcasts](https:\/\/cepr.org\/voxeu\/vox-webcasts)\n  - [Webinar Videos](https:\/\/cepr.org\/audio-and-video\/webinar-videos)\n- [About CEPR](https:\/\/cepr.org\/about)\n  - [People](https:\/\/cepr.org\/about\/people)\n  - [News](https:\/\/cepr.org\/about\/news)\n  - [Advisory Board](https:\/\/cepr.org\/about\/people\/advisory-board)\n  - [Press](https:\/\/cepr.org\/about\/press)\n  - [Governance](https:\/\/cepr.org\/about\/governance)\n  - [Support CEPR](https:\/\/cepr.org\/about\/join-cepr)\n  - [Partners](https:\/\/cepr.org\/partners)\n  - [Jobs at CEPR](https:\/\/cepr.org\/about\/jobs-at-cepr)\n\n## Secondary Menu\n- [CEPR Offices](https:\/\/cepr.org\/cepr-offices)\n\n## User account menu\n- [Log in](https:\/\/cepr.org\/moLogin)\n\n## Secondary Menu\n- [CEPR Offices](https:\/\/cepr.org\/cepr-offices)\n\n## User account menu\n- [Log in](https:\/\/cepr.org\/moLogin)\n\n## Themes\n- Key Themes\n- [Artificial Intelligence](https:\/\/cepr.org\/themes\/artificial-intelligence)\n- [Central Banking](https:\/\/cepr.org\/themes\/central-banking)\n- [Climate Change](https:\/\/cepr.org\/themes\/climate-change)\n- [Competition Policy](https:\/\/cepr.org\/themes\/competition-policy)\n- [Development & Growth](https:\/\/cepr.org\/themes\/development-growth)\n- [Economic History](https:\/\/cepr.org\/themes\/economic-history)\n- [Energy](https:\/\/cepr.org\/themes\/energy)\n- [EU Economic Architecture](https:\/\/cepr.org\/themes\/eu-economic-architecture \"EU Economic Architecture\")\n- [Inflation](https:\/\/cepr.org\/themes\/inflation)\n- [Political Economy](https:\/\/cepr.org\/themes\/political-economy \"Political Economy\")\n- [Poverty and income inequality](https:\/\/cepr.org\/themes\/poverty-and-income-inequality \"Poverty and income inequality\")\n- [Sustainable Finance](https:\/\/cepr.org\/themes\/sustainable-finance)\n- [Ukraine Initiative](https:\/\/cepr.org\/themes\/ukraine-initiative)\n- [Women in Economics](https:\/\/cepr.org\/themes\/women-economics)\n- Other Themes\n- [Business Cycles](https:\/\/cepr.org\/themes\/business-cycles)\n- [Covid 19](https:\/\/cepr.org\/themes\/covid-19)\n- [Exchange Rates](https:\/\/cepr.org\/themes\/exchange-rates)\n- [Finance & Fintech](https:\/\/cepr.org\/themes\/finance-and-fintech \"Finance & Fintech\")\n- [Health Economics](https:\/\/cepr.org\/themes\/health-economics)\n- [International Finance](https:\/\/cepr.org\/themes\/international-finance \"International Finance\")\n- [International Trade](https:\/\/cepr.org\/themes\/international-trade \"International Trade\")\n- [Labour Markets](https:\/\/cepr.org\/themes\/labour-markets)\n- [Migration](https:\/\/cepr.org\/themes\/migration \"Migration\")\n- [Monetary Policy](https:\/\/cepr.org\/themes\/monetary-policy \"Monetary Policy\")\n- [Productivity and Innovation](https:\/\/cepr.org\/themes\/productivity-and-innovation \"Productivity and Innovation\")\n- [Taxation](https:\/\/cepr.org\/themes\/taxation \"Taxation\")\n- Focus On\n- [The Economic Consequences of the War](https:\/\/cepr.org\/debates\/economic-consequences-war)\n- [Europe-Asia Initiative](https:\/\/cepr.org\/europe-asia-initiative)\n- [Trump & Tariffs](https:\/\/cepr.org\/voxeu\/trump-and-tariffs)\n\nSearch the site\n\n1. \/ [VoxEU](https:\/\/cepr.org\/voxeu)\n2. \/ [Search All Columns](https:\/\/cepr.org\/voxeu\/search-all-columns)\n\nIn this section\n\n- [Search All Columns](https:\/\/cepr.org\/voxeu\/search-all-columns)\n- [VoxTalks](https:\/\/cepr.org\/voxeu\/voxtalks)\n- [VoxEU Videos](https:\/\/cepr.org\/audio-and-video\/voxeu-videos)\n- [About VoxEU](https:\/\/cepr.org\/voxeu\/about-voxeu)\n\n![stock market heat map showing major U.S. tech companies](https:\/\/cepr.org\/sites\/default\/files\/styles\/16_9_small\/public\/2025-09\/AdobeStock_1322385575_Editorial_Use_Only.jpeg?itok=CYyv4Xmq)\n\nVoxEU Column  Financial Markets\n\n# Unpacking US tech valuations: An agnostic assessment\n- [Marco Albori](https:\/\/cepr.org\/about\/people\/marco-albori)\n- [Valerio Nispi Landi](https:\/\/cepr.org\/about\/people\/valerio-nispi-landi)\n- [Marco Taboga](https:\/\/cepr.org\/about\/people\/marco-taboga)\n- \/\n\n8 Sep 2025\n\nThe recent performance of the largest US tech companies has raised concerns about the risk of a stock market bubble. Using a three-stage pricing model applied to the top ten US tech firms, this column finds that current market prices do not appear implausible given the underlying drivers of both recent and expected performance. The adoption of AI technologies will be a major engine of growth, which is already transforming markets and displacing jobs. However, risks will remain around the ability to identify reliable AI applications and the growing competition from abroad.\n\n#### Authors\n[![](https:\/\/cepr.org\/sites\/default\/files\/styles\/portrait_small\/public\/profile-photos\/208527-208445_marco_albori_1dbdb114a574a3b39687513a61cf9f6c_29de97016cb8c7dca7bde57b9ac9e5b5.jpg?itok=cJ6TB4Nc)](https:\/\/cepr.org\/about\/people\/marco-albori \"Go to profile of Marco Albori\")\n\n### [Marco Albori](https:\/\/cepr.org\/about\/people\/marco-albori \"Go to profile of Marco Albori\")\n\n[![](https:\/\/cepr.org\/sites\/default\/files\/styles\/portrait_small\/public\/profile-photos\/184576-184576_valerio_nispi_landi_b9274da1487b4ec37f0ba20eee8706a4_ad68f9066f1efe5dec3325bbd7aec791.jpeg?itok=DmINHy8K)](https:\/\/cepr.org\/about\/people\/valerio-nispi-landi \"Go to profile of Valerio Nispi Landi\")\n\n### [Valerio Nispi Landi](https:\/\/cepr.org\/about\/people\/valerio-nispi-landi \"Go to profile of Valerio Nispi Landi\")\n\n[![](https:\/\/cepr.org\/sites\/default\/files\/styles\/portrait_small\/public\/profile-photos\/222560-taboga_77dcf1b82938f337d3f3b788c6b13d11.jpg?itok=GpzV-KEU)](https:\/\/cepr.org\/about\/people\/marco-taboga \"Go to profile of Marco Taboga\")\n\n### [Marco Taboga](https:\/\/cepr.org\/about\/people\/marco-taboga \"Go to profile of Marco Taboga\")\n\n#### Share\n- [Twitter](https:\/\/twitter.com\/intent\/tweet?text=Unpacking%20US%20tech%20valuations%3A%20An%20agnostic%20assessment&url=https%3A%2F%2Fcepr.org%2Fvoxeu%2Fcolumns%2Funpacking-us-tech-valuations-agnostic-assessment&via=cepr_org&hashtags= \"Share via Twitter\")\n- [Facebook](https:\/\/www.facebook.com\/sharer\/sharer.php?u=https%3A%2F%2Fcepr.org%2Fvoxeu%2Fcolumns%2Funpacking-us-tech-valuations-agnostic-assessment \"Share via Facebook\")\n- [LinkedIn](https:\/\/www.linkedin.com\/shareArticle?mini=true&url=https%3A%2F%2Fcepr.org%2Fvoxeu%2Fcolumns%2Funpacking-us-tech-valuations-agnostic-assessment&title=Unpacking%20US%20tech%20valuations%3A%20An%20agnostic%20assessment&source=CEPR&summary=The%20recent%20performance%20of%20the%20largest%20US%20tech%20companies%20has%20raised%20concerns%20about%20the%20risk%20of%20a%20stock%20market%20bubble.%20Using%20a%20three-stage%20pricing%20model%20applied%20to%20the%20top%20ten%20US%20tech%20firms,%20this%20column%20finds%20that%20current%20market%20prices%20do%20not%20appear%20implausible%20given%20the%20underlying%20drivers%20of%20both%20recent%20and%20expected%20performance.%20The%20adoption%20of%20AI%20technologies%20will%20be%20a%20major%20engine%20of%20growth,%20which%20is%20already%20transforming%20markets%20and%20displacing%20jobs.%20However,%20risks%20will%20remain%20around%20the%20ability%20to%20identify%20reliable%20AI%20applications%20and%20the%20growing%20competition%20from%20abroad. \"Share via LinkedIn\")\n- [Bluesky](https:\/\/bsky.app\/intent\/compose?text=Unpacking%20US%20tech%20valuations%3A%20An%20agnostic%20assessment%20https%3A%2F%2Fcepr.org%2Fvoxeu%2Fcolumns%2Funpacking-us-tech-valuations-agnostic-assessment \"Share via Bluesky\")\n\nInnovations in artificial intelligence (AI) have fuelled substantial stock price gains for listed US technology companies. Nvidia, the most focused on AI technology, has been the first company to reach a \\$4 trillion capitalisation at the beginning of July, followed by Microsoft three weeks later. The top ten US tech companies [1](https:\/\/cepr.org\/voxeu\/columns\/unpacking-us-tech-valuations-agnostic-assessment#footnote1_9joowpb \"Scroll down to this footnote\")  (henceforth, Mag10) now account for one-third and one-sixth of total US and global stock market capitalisations, respectively (Figure 1). [2](https:\/\/cepr.org\/voxeu\/columns\/unpacking-us-tech-valuations-agnostic-assessment#footnote2_jfw2q0b \"Scroll down to this footnote\")\n\nAlongside rising concentration, the elevated and growing valuations of US companies (as measured by their price\/earnings ratios; Figure 2) have been a top-of-mind concern for financial analysts, prompting them to debate whether the US stock market is currently experiencing a bubble like the dot-com era of the late 1990s (Marks 2025, Stacey and Novik 2025, Duguid et al. 2025).\n\nIn this column, we contribute to the debate by first presenting descriptive evidence on the valuations and fundamentals of the largest US tech companies. We then apply a simple three-stage pricing model to derive their market-implied expected profit growth rates. Finally, we discuss the plausibility of such expected growth rates.\n\n![Figures 1 and 2](https:\/\/cepr.org\/sites\/default\/files\/styles\/flexible_wysiwyg\/public\/2025-09\/landifig1.png?itok=RfVgN7vX)\n\n![Figures 1 and 2](https:\/\/cepr.org\/sites\/default\/files\/styles\/popup_small\/public\/2025-09\/landifig1.png?itok=9p1dqIiY)\n## Descriptive evidence\nPrice\/earnings (P\/E) ratios for the US stock market have been on an increasing trajectory for more than a decade, with a notable surge during the Covid period, and are indeed approaching the levels observed during the dot-com era (Figure 2). In contrast, t**he P\/E ratios of the largest tech companies remain well below the dot-com extremes, also because stock prices have not skyrocketed as spectacularly as they did in the late 1990s** (Figures 3 and 4).\n\n![Figures 3 and 4](https:\/\/cepr.org\/sites\/default\/files\/styles\/flexible_wysiwyg\/public\/2025-09\/landifig2.png?itok=tM_MzWtg)\n\n![Figures 3 and 4](https:\/\/cepr.org\/sites\/default\/files\/styles\/popup_small\/public\/2025-09\/landifig2.png?itok=7gNCmE1O)\n\n**By focusing on the Mag10, we find significant heterogeneity in their current and forward P\/E ratios** (between 20 and more than 600; Table 1). The smaller companies (Broadcom, Oracle, Palantir, and Tesla) are those that have the higher multiples. These are also the companies that have recently made – or are in the process of making – inroads into new, mostly AI-related businesses that have high growth potential and are expected to significantly change the level and composition of their earnings. [3](https:\/\/cepr.org\/voxeu\/columns\/unpacking-us-tech-valuations-agnostic-assessment#footnote3_ga9noy0 \"Scroll down to this footnote\")  The larger companies, instead, have much lower multiples. This may reflect investors’ expectations that their profits are unlikely to experience spectacular growth rates, given their already huge size and reliance – at least in part – on fairly traditional and saturated markets. Finally, Google’s relatively low P\/E ratio (around 20) seems to indicate that investors are not indiscriminately pouring money into AI companies: while Google has developed frontier and, in some cases, best-in-class AI models, it is not yet clear that its AI strategy is able to counter threats to its most lucrative business (Search), which might be displaced by the rapid change in users’ behaviour (LLM use instead of Internet navigation).\n\n**Table 1** Mag10 market capitalisation and price\/earnings multiples\n\n![Table 1 Mag10 market capitalisation and price\/earnings multiples](https:\/\/cepr.org\/sites\/default\/files\/styles\/flexible_wysiwyg\/public\/2025-09\/landitable1.png?itok=IsiNg2NM)\n\n![Table 1 Mag10 market capitalisation and price\/earnings multiples](https:\/\/cepr.org\/sites\/default\/files\/styles\/popup_small\/public\/2025-09\/landitable1.png?itok=P_a0sFCA)\n###### *Source*: LSEG. *Note*: Average values for 1-13 August 2025.\nThis preliminary descriptive evidence suggests that **investors are still significantly discriminating tech and AI-related firms by their idiosyncratic earnings prospects. Such discrimination tends to vanish during bubble episodes** and other periods when sentiment and herd behaviour substantially shape valuations (Baker and Wurgler 2006, Chang et al. 2000).\n\n## Evidence from an equilibrium dividend discount model\nA stock’s price should be equal to the present discounted value of its future dividends. Elevated P\/E ratios may signal optimism about future profit and dividend growth, optimism that could prove either well-founded or misplaced, thereby exposing the market to the risk of abrupt corrections. In Albori et al. (forthcoming), we use an equilibrium dividend discount model (Molodovsky et al. 1965, Fuller and Hsia 1984, Sorensen and Williamson 1985) **to compute the abnormal growth rates of earnings that would justify current equity valuations, that is, make them compatible with rational pricing in line with historical norms**. Table 2 shows the model-implied growth rates, together with recent earnings growth rates and analysts’ expected growth rates, all inflation-adjusted.\n\n**Table 2** Mag10 recent, expected, and model-implied growth in earnings per share (percent)\n\n![Table 2 Mag10 recent, expected, and model-implied growth in earnings per share](https:\/\/cepr.org\/sites\/default\/files\/styles\/flexible_wysiwyg\/public\/2025-09\/landitable2.png?itok=M2uwey4y)\n\n![Table 2 Mag10 recent, expected, and model-implied growth in earnings per share](https:\/\/cepr.org\/sites\/default\/files\/styles\/popup_small\/public\/2025-09\/landitable2.png?itok=ZZ4ojHK9)\n###### *Source*: LSEG, our estimates. *Note*: Real (inflation-adjusted) annual growth rates in earnings per share (EPS). For Tesla, historical EPS growth is over the past four years; for Palantir, the past year. Growth expected by I\/B\/E\/S analysts refers to the average growth rate over a period of three to five years. Model-implied growth rates are derived from P\/E ratios as recorded on 28 July 2025.\n**For the six largest companies in our sample, the average model-implied annual growth rate is 12%**, with moderate dispersion. **This indicates a significant deceleration from the 33% average growth observed over the past five years** and is also more conservative than the 15% growth expected by analysts.\n\n**For the remaining four companies, the mean model-implied growth rate is 41%**, which compares with 39% in the previous five years and 17% expected by analysts. As discussed in the previous section, these four companies are radically different from the other six, in that they are much smaller and have recently entered fast-growing AI-related markets. **Their implied growth rates are compatible with scenarios – far from being guaranteed, but also not utterly unrealistic – in which they manage to scale up their new business lines at a pace similar to that kept in recent years.**\n\nIn summary, current equity valuations for the top US tech companies are rational if one expects high, but not historically unusual growth rates for the coming years. Apart from historical comparisons, determining if these expected growth rates are realistic is challenging. This assessment relies heavily on subjective judgments about market developments, particularly the future demand for AI-related services and improvements in productivity and profitability driven by AI adoption. In the following section, we present information that may help inform these judgments.\n\n## Factors affecting future earnings growth\nThe main factors that contributed to the spectacular earnings growth of US tech companies in recent years (Figures 5 and 6) were:\n\n- **Strong increases in global spending on digital advertisements** (by 15% annually on average between 2019 and 2024; Kemp 2025), often driven by the displacement of traditional advertisements (e.g. on TV and newspapers) and by the rise of e-commerce at the expense of brick-and-mortar stores. Amazon, Google, and Meta are market leaders in this space and have continued to expand their market shares (in 2024, they accounted for 51% of global ad sales; BestMediaInfo Bureau 2025).\n- **Increased corporate spending on both traditional and AI-related cloud services** (up by 21% annually on average between 2019 and 2024; Slingerland 2025). Amazon, Google, and Microsoft are the main providers of these services, with a combined global market share of 63% (Brindley and Zhang 2025). Significant fractions of the hardware bought by these players to scale up their data centres is provided by NVIDIA and Broadcom.\n- **Still rapid global expansion of the number of smartphones** (+5% average yearly growth between 2019 and 2024), coupled with the continued increase in the amount of time spent by users in the interaction with these devices (Howarth 2025). This has sustained the sales of phones (Apple, Google) and consumer-oriented cloud services (Apple, Google, Microsoft), and has helped fuel the previous two trends (spending on digital ads and cloud infrastructure).\n- **Gains in efficiency driven by economies of scale and AI adoption.** For example, software development represents a significant fraction of the cost of offering the products and services mentioned above, but its cost tends to increase less than proportionally as the user base expands (the same piece of software serves more users). Moreover, AI has already facilitated the automation of labour-intensive tasks, such as content-moderation on social platforms (Meta). Reportedly, AI has also improved the economics of digital advertising, by allowing for a better targeting of users.\n\n![Figures 5 and 6](https:\/\/cepr.org\/sites\/default\/files\/styles\/flexible_wysiwyg\/public\/2025-09\/landifig3.png?itok=64a1MZbq)\n\n![Figures 5 and 6](https:\/\/cepr.org\/sites\/default\/files\/styles\/popup_small\/public\/2025-09\/landifig3.png?itok=0AQdo6oC)\n\nAccording to the latest quarterly reports of the Mag10 (which on average showed still sustained growth), as well as to the expert sources cited above, **none of these trends is expected to end abruptly in the near future, although some of them are expected to slow down.** For example, smartphone adoption is expected to be sustained mostly by emerging markets, as advanced ones have already been saturated (Bellan 2025). Similarly, future growth in digital-advertisement sales may be hindered by the already large market shares held by the Mag10, although also in this case emerging markets represent a significant expansion opportunity.\n\n**The growth prospects of cloud services (and related hardware) are probably the most controversial ones.** While some analysts expect AI adoption to keep propping up cloud spending for the foreseeable future (Goldman Sachs 2024), others deem that current AI spending by corporations is bubbly and likely to slow down, as the economics of AI applications are often unproven (Widder and Hicks 2025). Whatever the stance on this issue, most analysts seem to agree that AI spending is the key factor that will determine whether tech companies are able to achieve the high growth rates envisaged by financial analysts and embedded in stock prices (e.g. our model-implied estimates).\n\nWhile taking a position in this debate is complex, we note that **some recent trends seem to support the view that the demand for AI services will keep increasing at a brisk pace:**\n\n- As noted in a recent Economist article, the use of large language models (LLMs), such as ChatGPT, is quickly displacing Internet navigation (15% search traffic drop in the past year), as users find it more convenient to receive responses to their queries from an LLM rather than sifting through multiple websites (The Economist 2025a). This trend has just started, and it is likely to continue. The consequence is that a large portion of the revenues and profits previously made by millions of web-site owners and content creators will be shifted to cloud-service providers and hardware vendors (Godoy 2025, Weiss 2024), as on-the-fly content creation by LLMs requires enormous amounts of computational resources. At present, some of the major AI players still report difficulties in meeting these computational requirements, despite the huge investments in infrastructure made in previous years.\n- Job destruction by generative AI technologies (not only LLMs, but also image and video generators) is reportedly gaining traction and economic significance not only in the website-ownership space, but also in adjacent occupations (ad creatives, illustrators, writers and copywriters, photographers, models, translators, moderators, etc.) (Bartholomew 2025). For example, according to a 2024 survey conducted by the Society of Authors (SOA Policy Team (2024), 26% of illustrators and 36% of translators report having already lost work due to generative AI, and three-quarters of them expect future income reductions. As explained in the previous point, this kind of displacement creates demand for the hardware and cloud services sold by the Mag10.\n- AI technology is being increasingly adopted in the automotive sector (not only in autonomous vehicles, but also for driving assistance and infotainment). This trend – which will also significantly prop up the demand for cloud services, given the huge size of the automotive sector – has begun only recently, and it seems unlikely to abate, given that most auto-industry executives expect vehicles to become increasingly ‘software-defined’ (IBM 2025).\n\nThese are just some examples of large markets where substantial AI adoption and, in some cases, the consequent displacement of jobs are not just a future possibility, but something that is already happening at scale. Mechanically, this adoption generates demand for compute, which is mostly offered by large US tech firms.\n\n## Conclusions\nThe largest US technology companies have recently recorded substantial increases in their valuations, prompting financial analysts and policymakers to express concerns about the risk of a stock market bubble. In this column, we present model-based evidence suggesting that the earnings growth rates required to justify current prices do not appear implausible given the underlying drivers of recent and expected performance. We also argue that the adoption of generative AI technologies will be a major engine of growth, and it is already transforming markets and displacing jobs at scale. Its rollout fuels rising demand for computing power, largely supplied by US tech companies, reinforcing their dominance. However, some cautions are in order. Some industries still struggle to identify reliable applications, sparking short-term disillusionment. This could temporarily curb corporate AI spending and drive a re-pricing of technology stocks (The Economist 2025b). Added to this, intensifying competition, particularly from China, threatens to erode big tech’s profit margins (Mims 2025). These factors could make the realisation of the growth rates implied by our model less certain.\n\n*Authors’ note: The opinions expressed in this column are those of the authors and do not necessarily reflect the views of the Bank of Italy.*\n\n## References\nAlbori, M, V Nispi Landi and M Taboga (forthcoming), “Is there a bubble in the US stock market? Evidence from an agnostic valuation procedure”, Bank of Italy Occasional Papers.\n\nBaker, M and J Wurgler (2006), “Investor sentiment and the cross‐section of stock returns”, *The Journal of Finance* 61: 1645-1680.\n\nBartholomew, J (2025), “It’s happening fast’ – creative workers and professionals share their fears and hopes about the rise of AI”, *The Guardian.*\n\nBellan, R (2025), “Apple: Pay attention to emerging markets, not falling China sales”, TechCrunch.\n\nBestMediaInfo Bureau (2025), “Google, Meta, Amazon’s combined share of global ad revenues hits 51% in 2024: Magna”.\n\nBrindley, R and Y Zhang (2025), “Worldwide cloud service spending to grow by 19% in 2025”, Canalys.\n\nChang, E C, J W Cheng and A Khorana (2000), “An examination of herd behavior in equity markets: An international perspective”, *Journal of Banking & Finance* 24: 1651-1679.\n\nDuguid, K, G Steer, H Clarfelt and I Smith (2025), “Wall Street ‘euphoria’ sparks bubble warnings”, *Financial Times.*\n\nFuller, R J and C C Hsia (1984), “A simplified common stock valuation model”, *Financial Analysts Journal* 40: 49-56.\n\nGodoy, J (2025), “Google's AI previews erode the internet, US edtech company says in lawsuit”, Reuters.\n\nGoldman Sachs (2024), “Cloud revenues poised to reach \\$2 trillion by 2030 amid AI rollout”, GS Artificial Intelligence.\n\nHowarth, J (2025), “How Many People Own Smartphones?”, Exploding Topics.\n\nIBM (2025), “Automotive in the AI era”, IBM Institute for Business Value.\n\nKemp, S (2025), “Digital 2025: Global advertising trends”, DataReportal.\n\nMarks, H (2025), “Is the US stock market in bubble territory?”, *Financial Times*.\n\nMims, C (2025), “Silicon Valley’s New Strategy: Move Slow and Build Things”, The Wall Street Journal.\n\nMolodovsky, N, C May and S Chottiner (1965), “Common stock valuation: Principles, tables and application”, *Financial Analysts Journal* 21: 104–123.\n\nSlingerland, C (2025), “90+ Cloud Computing Statistics: A 2025 Market Snapshot”, CloudZero.\n\nSOA Policy Team (2024), “SoA survey reveals a third of translators and quarter of illustrators losing work to AI”, The Society of Authors.\n\nSorensen, E H and D A Williamson (1985), “Some evidence on the value of dividend discount models”, *Financial Analysts Journal* 4: 60-69.\n\nStacey, S and M Novik (2025), “How big is the stock market’s America bubble?”, *Financial Times.*\n\n*The Economist* (2025a), “AI is killing the web. Can anything save it?”.\n\n*The Economist* (2025b), “Welcome to the AI trough of disillusionment”.\n\nWeiss, T (2025), “The Decline of Stack Overflow”, Data Science with R.\n\nWidder, D G and M Hicks (2025), “Watching the Generative AI Hype Bubble Deflate”, Digital Life Initiative, Cornell University.\n\n#### Footnotes\n1. Alphabet (Google), Amazon, Apple, Broadcom, Meta, Microsoft, Nvidia, Oracle, Palantir and Tesla.\n2. Based respectively on Datastream’s US Total Market Index (including about one thousand constituent stocks) and World Total Market Index (including more than 7000).\n3. Broadcom has recently become a key supplier of AI chips that offer an alternative to NVIDIA’s silicon. Oracle, traditionally a supplier of database software, has successfully scaled up its cloud data centre business, which is deemed able to compete with those of major cloud-services vendors such as Amazon. Palantir, which previously provided database-integration services mostly to defence and law-enforcement contractors, has significantly expanded its offerings with AI services that appeal to large corporations. Tesla is continuing to develop autonomous driving and a humanoid robot that – in case of commercialisation – could open huge markets.\n\n#### Authors\n[![](https:\/\/cepr.org\/sites\/default\/files\/styles\/portrait_small\/public\/profile-photos\/208527-208445_marco_albori_1dbdb114a574a3b39687513a61cf9f6c_29de97016cb8c7dca7bde57b9ac9e5b5.jpg?itok=cJ6TB4Nc)](https:\/\/cepr.org\/about\/people\/marco-albori \"Go to profile of Marco Albori\")\n\n### [Marco Albori](https:\/\/cepr.org\/about\/people\/marco-albori \"Go to profile of Marco Albori\")\nEconomist Bank Of Italy\n\n[![](https:\/\/cepr.org\/sites\/default\/files\/styles\/portrait_small\/public\/profile-photos\/184576-184576_valerio_nispi_landi_b9274da1487b4ec37f0ba20eee8706a4_ad68f9066f1efe5dec3325bbd7aec791.jpeg?itok=DmINHy8K)](https:\/\/cepr.org\/about\/people\/valerio-nispi-landi \"Go to profile of Valerio Nispi Landi\")\n\n### [Valerio Nispi Landi](https:\/\/cepr.org\/about\/people\/valerio-nispi-landi \"Go to profile of Valerio Nispi Landi\")\nEconomist Bank Of Italy\n\n[![](https:\/\/cepr.org\/sites\/default\/files\/styles\/portrait_small\/public\/profile-photos\/222560-taboga_77dcf1b82938f337d3f3b788c6b13d11.jpg?itok=GpzV-KEU)](https:\/\/cepr.org\/about\/people\/marco-taboga \"Go to profile of Marco Taboga\")\n\n### [Marco Taboga](https:\/\/cepr.org\/about\/people\/marco-taboga \"Go to profile of Marco Taboga\")\nHead of the International Finance and Commodity Markets Division Bank Of Italy\n\n#### Themes\n- [Financial Markets](https:\/\/cepr.org\/themes\/financial-markets)\n\n#### Keywords\n- [tech bubble](https:\/\/cepr.org\/search?keyword%5B365270%5D=365270)\n\n#### Share\n- [Twitter](https:\/\/twitter.com\/intent\/tweet?text=Unpacking%20US%20tech%20valuations%3A%20An%20agnostic%20assessment&url=https%3A%2F%2Fcepr.org%2Fvoxeu%2Fcolumns%2Funpacking-us-tech-valuations-agnostic-assessment&via=cepr_org&hashtags= \"Share via Twitter\")\n- [Facebook](https:\/\/www.facebook.com\/sharer\/sharer.php?u=https%3A%2F%2Fcepr.org%2Fvoxeu%2Fcolumns%2Funpacking-us-tech-valuations-agnostic-assessment \"Share via Facebook\")\n- [LinkedIn](https:\/\/www.linkedin.com\/shareArticle?mini=true&url=https%3A%2F%2Fcepr.org%2Fvoxeu%2Fcolumns%2Funpacking-us-tech-valuations-agnostic-assessment&title=Unpacking%20US%20tech%20valuations%3A%20An%20agnostic%20assessment&source=CEPR&summary=The%20recent%20performance%20of%20the%20largest%20US%20tech%20companies%20has%20raised%20concerns%20about%20the%20risk%20of%20a%20stock%20market%20bubble.%20Using%20a%20three-stage%20pricing%20model%20applied%20to%20the%20top%20ten%20US%20tech%20firms,%20this%20column%20finds%20that%20current%20market%20prices%20do%20not%20appear%20implausible%20given%20the%20underlying%20drivers%20of%20both%20recent%20and%20expected%20performance.%20The%20adoption%20of%20AI%20technologies%20will%20be%20a%20major%20engine%20of%20growth,%20which%20is%20already%20transforming%20markets%20and%20displacing%20jobs.%20However,%20risks%20will%20remain%20around%20the%20ability%20to%20identify%20reliable%20AI%20applications%20and%20the%20growing%20competition%20from%20abroad. \"Share via LinkedIn\")\n- [Bluesky](https:\/\/bsky.app\/intent\/compose?text=Unpacking%20US%20tech%20valuations%3A%20An%20agnostic%20assessment%20https%3A%2F%2Fcepr.org%2Fvoxeu%2Fcolumns%2Funpacking-us-tech-valuations-agnostic-assessment \"Share via Bluesky\")\n\n[![person talking with robot](https:\/\/cepr.org\/sites\/default\/files\/styles\/16_9_small\/public\/2023-06\/AdobeStock_571280527.jpeg?itok=JJhMDCms)](https:\/\/cepr.org\/voxeu\/columns\/generative-ai-and-firm-valuation \"Generative AI and firm valuation\")\n\nVoxEU Column\n\n### [Generative AI and firm valuation](https:\/\/cepr.org\/voxeu\/columns\/generative-ai-and-firm-valuation)\n- [Andrea Eisfeldt](https:\/\/cepr.org\/about\/people\/andrea-eisfeldt)\n- [Gregor Schubert](https:\/\/cepr.org\/about\/people\/gregor-schubert-0)\n- [Miao Ben Zhang](https:\/\/cepr.org\/about\/people\/miao-ben-zhang)\n\n4 Jun 2023\n\n- [Labour Markets](https:\/\/cepr.org\/themes\/labour-markets \"Go to the Labour Markets page\")\n- [Productivity and Innovation](https:\/\/cepr.org\/themes\/productivity-and-innovation \"Go to the Productivity and Innovation page\")\n\n[![AdobeStock\\_250694777.jpeg](https:\/\/cepr.org\/sites\/default\/files\/styles\/16_9_small\/public\/voxeu-cover-image\/AdobeStock_250694777.jpeg?itok=PQ4Dc5YL)](https:\/\/cepr.org\/voxeu\/columns\/beware-tech-bubbles-long-term-earnings-dot-com-bubble-generation \"Beware of tech bubbles: Long-term earnings of the dot-com bubble generation\")\n\nVoxEU Column\n\n### [Beware of tech bubbles: Long-term earnings of the dot-com bubble generation](https:\/\/cepr.org\/voxeu\/columns\/beware-tech-bubbles-long-term-earnings-dot-com-bubble-generation)\n- [Adrien Matray](https:\/\/cepr.org\/about\/people\/adrien-matray)\n- [Johan Hombert](https:\/\/cepr.org\/about\/people\/johan-hombert)\n\n5 Jan 2020\n\n- [Labour Markets](https:\/\/cepr.org\/themes\/labour-markets \"Go to the Labour Markets page\")\n\n[![Microchip with brain illustration ](https:\/\/cepr.org\/sites\/default\/files\/styles\/16_9_small\/public\/2023-06\/AdobeStock_159300418.jpeg?itok=qpEqAnT7)](https:\/\/cepr.org\/voxeu\/columns\/impact-artificial-intelligence-growth-and-employment \"The impact of artificial intelligence on growth and employment\")\n\nVoxEU Column\n\n### [The impact of artificial intelligence on growth and employment](https:\/\/cepr.org\/voxeu\/columns\/impact-artificial-intelligence-growth-and-employment)\n- [Ethan Ilzetzki](https:\/\/cepr.org\/about\/people\/ethan-ilzetzki)\n- [Suryaansh Jain](https:\/\/cepr.org\/about\/people\/suryaansh-jain)\n\n20 Jun 2023\n\n- [Labour Markets](https:\/\/cepr.org\/themes\/labour-markets \"Go to the Labour Markets page\")\n- [Productivity and Innovation](https:\/\/cepr.org\/themes\/productivity-and-innovation \"Go to the Productivity and Innovation page\")\n\n- Themes & Current Issues\n- [Artificial Intelligence](https:\/\/cepr.org\/themes\/artificial-intelligence)\n- [Business Cycles](https:\/\/cepr.org\/themes\/business-cycles)\n- [Central Banking](https:\/\/cepr.org\/themes\/central-banking)\n- [Climate Change](https:\/\/cepr.org\/themes\/climate-change)\n- [Competition Policy](https:\/\/cepr.org\/themes\/competition-policy)\n- [COVID-19](https:\/\/cepr.org\/themes\/covid-19)\n- [Development & Growth](https:\/\/cepr.org\/themes\/development-growth)\n- [Economic history](https:\/\/cepr.org\/themes\/economic-history)\n- [Energy](https:\/\/cepr.org\/themes\/energy)\n- [EU Economic Architecture](https:\/\/cepr.org\/themes\/eu-economic-architecture)\n- [Exchange Rates](https:\/\/cepr.org\/themes\/exchange-rates)\n- [Finance and Fintech](https:\/\/cepr.org\/themes\/finance-and-fintech)\n- [Financial Markets](https:\/\/cepr.org\/themes\/financial-markets)\n- [Financial Regulation and Banking](https:\/\/cepr.org\/themes\/financial-regulation-and-banking)\n- [Health Economics](https:\/\/cepr.org\/themes\/health-economics)\n- [Inflation](https:\/\/cepr.org\/themes\/inflation)\n- [International Finance](https:\/\/cepr.org\/themes\/international-finance)\n- [International trade](https:\/\/cepr.org\/themes\/international-trade)\n- [Labour Markets](https:\/\/cepr.org\/themes\/labour-markets)\n- [Migration](https:\/\/cepr.org\/themes\/migration)\n- [Monetary Policy](https:\/\/cepr.org\/themes\/monetary-policy)\n- [Political Economy](https:\/\/cepr.org\/themes\/political-economy)\n- [Poverty and Income Inequality](https:\/\/cepr.org\/themes\/poverty-and-income-inequality)\n- [Productivity and Innovation](https:\/\/cepr.org\/themes\/productivity-and-innovation)\n- [Taxation](https:\/\/cepr.org\/themes\/taxation)\n- [Ukraine Initiative](https:\/\/cepr.org\/themes\/ukraine-initiative)\n- [Women in Economics](https:\/\/cepr.org\/themes\/women-economics)\n\n###### Sign up to our newsletter\n###### follow us\nCopyright 2026 CEPR \/[designbysoapbox.com](https:\/\/www.designbysoapbox.com\/)\n\n- [Contact Us](https:\/\/cepr.org\/contact-us \"How to contact us\")\n- [Cookies](https:\/\/cepr.org\/cookies)\n- [Privacy Policy](https:\/\/cepr.org\/privacy-policy)\n- [RSS Feeds](https:\/\/cepr.org\/rss-feeds)","attrs_readable_markdown":"Innovations in artificial intelligence (AI) have fuelled substantial stock price gains for listed US technology companies. Nvidia, the most focused on AI technology, has been the first company to reach a \\$4 trillion capitalisation at the beginning of July, followed by Microsoft three weeks later. The top ten US tech companies [1](https:\/\/cepr.org\/voxeu\/columns\/unpacking-us-tech-valuations-agnostic-assessment#footnote1_9joowpb \"Scroll down to this footnote\")  (henceforth, Mag10) now account for one-third and one-sixth of total US and global stock market capitalisations, respectively (Figure 1). [2](https:\/\/cepr.org\/voxeu\/columns\/unpacking-us-tech-valuations-agnostic-assessment#footnote2_jfw2q0b \"Scroll down to this footnote\")\n\nAlongside rising concentration, the elevated and growing valuations of US companies (as measured by their price\/earnings ratios; Figure 2) have been a top-of-mind concern for financial analysts, prompting them to debate whether the US stock market is currently experiencing a bubble like the dot-com era of the late 1990s (Marks 2025, Stacey and Novik 2025, Duguid et al. 2025).\n\nIn this column, we contribute to the debate by first presenting descriptive evidence on the valuations and fundamentals of the largest US tech companies. We then apply a simple three-stage pricing model to derive their market-implied expected profit growth rates. Finally, we discuss the plausibility of such expected growth rates.\n\n## Descriptive evidence\nPrice\/earnings (P\/E) ratios for the US stock market have been on an increasing trajectory for more than a decade, with a notable surge during the Covid period, and are indeed approaching the levels observed during the dot-com era (Figure 2). In contrast, t**he P\/E ratios of the largest tech companies remain well below the dot-com extremes, also because stock prices have not skyrocketed as spectacularly as they did in the late 1990s** (Figures 3 and 4).\n\n**By focusing on the Mag10, we find significant heterogeneity in their current and forward P\/E ratios** (between 20 and more than 600; Table 1). The smaller companies (Broadcom, Oracle, Palantir, and Tesla) are those that have the higher multiples. These are also the companies that have recently made – or are in the process of making – inroads into new, mostly AI-related businesses that have high growth potential and are expected to significantly change the level and composition of their earnings. [3](https:\/\/cepr.org\/voxeu\/columns\/unpacking-us-tech-valuations-agnostic-assessment#footnote3_ga9noy0 \"Scroll down to this footnote\")  The larger companies, instead, have much lower multiples. This may reflect investors’ expectations that their profits are unlikely to experience spectacular growth rates, given their already huge size and reliance – at least in part – on fairly traditional and saturated markets. Finally, Google’s relatively low P\/E ratio (around 20) seems to indicate that investors are not indiscriminately pouring money into AI companies: while Google has developed frontier and, in some cases, best-in-class AI models, it is not yet clear that its AI strategy is able to counter threats to its most lucrative business (Search), which might be displaced by the rapid change in users’ behaviour (LLM use instead of Internet navigation).\n\n**Table 1** Mag10 market capitalisation and price\/earnings multiples\n\n###### *Source*: LSEG. *Note*: Average values for 1-13 August 2025.\nThis preliminary descriptive evidence suggests that **investors are still significantly discriminating tech and AI-related firms by their idiosyncratic earnings prospects. Such discrimination tends to vanish during bubble episodes** and other periods when sentiment and herd behaviour substantially shape valuations (Baker and Wurgler 2006, Chang et al. 2000).\n\n## Evidence from an equilibrium dividend discount model\nA stock’s price should be equal to the present discounted value of its future dividends. Elevated P\/E ratios may signal optimism about future profit and dividend growth, optimism that could prove either well-founded or misplaced, thereby exposing the market to the risk of abrupt corrections. In Albori et al. (forthcoming), we use an equilibrium dividend discount model (Molodovsky et al. 1965, Fuller and Hsia 1984, Sorensen and Williamson 1985) **to compute the abnormal growth rates of earnings that would justify current equity valuations, that is, make them compatible with rational pricing in line with historical norms**. Table 2 shows the model-implied growth rates, together with recent earnings growth rates and analysts’ expected growth rates, all inflation-adjusted.\n\n**Table 2** Mag10 recent, expected, and model-implied growth in earnings per share (percent)\n\n###### *Source*: LSEG, our estimates. *Note*: Real (inflation-adjusted) annual growth rates in earnings per share (EPS). For Tesla, historical EPS growth is over the past four years; for Palantir, the past year. Growth expected by I\/B\/E\/S analysts refers to the average growth rate over a period of three to five years. Model-implied growth rates are derived from P\/E ratios as recorded on 28 July 2025.\n**For the six largest companies in our sample, the average model-implied annual growth rate is 12%**, with moderate dispersion. **This indicates a significant deceleration from the 33% average growth observed over the past five years** and is also more conservative than the 15% growth expected by analysts.\n\n**For the remaining four companies, the mean model-implied growth rate is 41%**, which compares with 39% in the previous five years and 17% expected by analysts. As discussed in the previous section, these four companies are radically different from the other six, in that they are much smaller and have recently entered fast-growing AI-related markets. **Their implied growth rates are compatible with scenarios – far from being guaranteed, but also not utterly unrealistic – in which they manage to scale up their new business lines at a pace similar to that kept in recent years.**\n\nIn summary, current equity valuations for the top US tech companies are rational if one expects high, but not historically unusual growth rates for the coming years. Apart from historical comparisons, determining if these expected growth rates are realistic is challenging. This assessment relies heavily on subjective judgments about market developments, particularly the future demand for AI-related services and improvements in productivity and profitability driven by AI adoption. In the following section, we present information that may help inform these judgments.\n\n## Factors affecting future earnings growth\nThe main factors that contributed to the spectacular earnings growth of US tech companies in recent years (Figures 5 and 6) were:\n\n- **Strong increases in global spending on digital advertisements** (by 15% annually on average between 2019 and 2024; Kemp 2025), often driven by the displacement of traditional advertisements (e.g. on TV and newspapers) and by the rise of e-commerce at the expense of brick-and-mortar stores. Amazon, Google, and Meta are market leaders in this space and have continued to expand their market shares (in 2024, they accounted for 51% of global ad sales; BestMediaInfo Bureau 2025).\n- **Increased corporate spending on both traditional and AI-related cloud services** (up by 21% annually on average between 2019 and 2024; Slingerland 2025). Amazon, Google, and Microsoft are the main providers of these services, with a combined global market share of 63% (Brindley and Zhang 2025). Significant fractions of the hardware bought by these players to scale up their data centres is provided by NVIDIA and Broadcom.\n- **Still rapid global expansion of the number of smartphones** (+5% average yearly growth between 2019 and 2024), coupled with the continued increase in the amount of time spent by users in the interaction with these devices (Howarth 2025). This has sustained the sales of phones (Apple, Google) and consumer-oriented cloud services (Apple, Google, Microsoft), and has helped fuel the previous two trends (spending on digital ads and cloud infrastructure).\n- **Gains in efficiency driven by economies of scale and AI adoption.** For example, software development represents a significant fraction of the cost of offering the products and services mentioned above, but its cost tends to increase less than proportionally as the user base expands (the same piece of software serves more users). Moreover, AI has already facilitated the automation of labour-intensive tasks, such as content-moderation on social platforms (Meta). Reportedly, AI has also improved the economics of digital advertising, by allowing for a better targeting of users.\n\nAccording to the latest quarterly reports of the Mag10 (which on average showed still sustained growth), as well as to the expert sources cited above, **none of these trends is expected to end abruptly in the near future, although some of them are expected to slow down.** For example, smartphone adoption is expected to be sustained mostly by emerging markets, as advanced ones have already been saturated (Bellan 2025). Similarly, future growth in digital-advertisement sales may be hindered by the already large market shares held by the Mag10, although also in this case emerging markets represent a significant expansion opportunity.\n\n**The growth prospects of cloud services (and related hardware) are probably the most controversial ones.** While some analysts expect AI adoption to keep propping up cloud spending for the foreseeable future (Goldman Sachs 2024), others deem that current AI spending by corporations is bubbly and likely to slow down, as the economics of AI applications are often unproven (Widder and Hicks 2025). Whatever the stance on this issue, most analysts seem to agree that AI spending is the key factor that will determine whether tech companies are able to achieve the high growth rates envisaged by financial analysts and embedded in stock prices (e.g. our model-implied estimates).\n\nWhile taking a position in this debate is complex, we note that **some recent trends seem to support the view that the demand for AI services will keep increasing at a brisk pace:**\n\n- As noted in a recent Economist article, the use of large language models (LLMs), such as ChatGPT, is quickly displacing Internet navigation (15% search traffic drop in the past year), as users find it more convenient to receive responses to their queries from an LLM rather than sifting through multiple websites (The Economist 2025a). This trend has just started, and it is likely to continue. The consequence is that a large portion of the revenues and profits previously made by millions of web-site owners and content creators will be shifted to cloud-service providers and hardware vendors (Godoy 2025, Weiss 2024), as on-the-fly content creation by LLMs requires enormous amounts of computational resources. At present, some of the major AI players still report difficulties in meeting these computational requirements, despite the huge investments in infrastructure made in previous years.\n- Job destruction by generative AI technologies (not only LLMs, but also image and video generators) is reportedly gaining traction and economic significance not only in the website-ownership space, but also in adjacent occupations (ad creatives, illustrators, writers and copywriters, photographers, models, translators, moderators, etc.) (Bartholomew 2025). For example, according to a 2024 survey conducted by the Society of Authors (SOA Policy Team (2024), 26% of illustrators and 36% of translators report having already lost work due to generative AI, and three-quarters of them expect future income reductions. As explained in the previous point, this kind of displacement creates demand for the hardware and cloud services sold by the Mag10.\n- AI technology is being increasingly adopted in the automotive sector (not only in autonomous vehicles, but also for driving assistance and infotainment). This trend – which will also significantly prop up the demand for cloud services, given the huge size of the automotive sector – has begun only recently, and it seems unlikely to abate, given that most auto-industry executives expect vehicles to become increasingly ‘software-defined’ (IBM 2025).\n\nThese are just some examples of large markets where substantial AI adoption and, in some cases, the consequent displacement of jobs are not just a future possibility, but something that is already happening at scale. Mechanically, this adoption generates demand for compute, which is mostly offered by large US tech firms.\n\n## Conclusions\nThe largest US technology companies have recently recorded substantial increases in their valuations, prompting financial analysts and policymakers to express concerns about the risk of a stock market bubble. In this column, we present model-based evidence suggesting that the earnings growth rates required to justify current prices do not appear implausible given the underlying drivers of recent and expected performance. We also argue that the adoption of generative AI technologies will be a major engine of growth, and it is already transforming markets and displacing jobs at scale. Its rollout fuels rising demand for computing power, largely supplied by US tech companies, reinforcing their dominance. However, some cautions are in order. Some industries still struggle to identify reliable applications, sparking short-term disillusionment. This could temporarily curb corporate AI spending and drive a re-pricing of technology stocks (The Economist 2025b). Added to this, intensifying competition, particularly from China, threatens to erode big tech’s profit margins (Mims 2025). These factors could make the realisation of the growth rates implied by our model less certain.\n\n*Authors’ note: The opinions expressed in this column are those of the authors and do not necessarily reflect the views of the Bank of Italy.*\n\n## References\nAlbori, M, V Nispi Landi and M Taboga (forthcoming), “Is there a bubble in the US stock market? Evidence from an agnostic valuation procedure”, Bank of Italy Occasional Papers.\n\nBaker, M and J Wurgler (2006), “Investor sentiment and the cross‐section of stock returns”, *The Journal of Finance* 61: 1645-1680.\n\nBartholomew, J (2025), “It’s happening fast’ – creative workers and professionals share their fears and hopes about the rise of AI”, *The Guardian.*\n\nBellan, R (2025), “Apple: Pay attention to emerging markets, not falling China sales”, TechCrunch.\n\nBestMediaInfo Bureau (2025), “Google, Meta, Amazon’s combined share of global ad revenues hits 51% in 2024: Magna”.\n\nBrindley, R and Y Zhang (2025), “Worldwide cloud service spending to grow by 19% in 2025”, Canalys.\n\nChang, E C, J W Cheng and A Khorana (2000), “An examination of herd behavior in equity markets: An international perspective”, *Journal of Banking & Finance* 24: 1651-1679.\n\nDuguid, K, G Steer, H Clarfelt and I Smith (2025), “Wall Street ‘euphoria’ sparks bubble warnings”, *Financial Times.*\n\nFuller, R J and C C Hsia (1984), “A simplified common stock valuation model”, *Financial Analysts Journal* 40: 49-56.\n\nGodoy, J (2025), “Google's AI previews erode the internet, US edtech company says in lawsuit”, Reuters.\n\nGoldman Sachs (2024), “Cloud revenues poised to reach \\$2 trillion by 2030 amid AI rollout”, GS Artificial Intelligence.\n\nHowarth, J (2025), “How Many People Own Smartphones?”, Exploding Topics.\n\nIBM (2025), “Automotive in the AI era”, IBM Institute for Business Value.\n\nKemp, S (2025), “Digital 2025: Global advertising trends”, DataReportal.\n\nMarks, H (2025), “Is the US stock market in bubble territory?”, *Financial Times*.\n\nMims, C (2025), “Silicon Valley’s New Strategy: Move Slow and Build Things”, The Wall Street Journal.\n\nMolodovsky, N, C May and S Chottiner (1965), “Common stock valuation: Principles, tables and application”, *Financial Analysts Journal* 21: 104–123.\n\nSlingerland, C (2025), “90+ Cloud Computing Statistics: A 2025 Market Snapshot”, CloudZero.\n\nSOA Policy Team (2024), “SoA survey reveals a third of translators and quarter of illustrators losing work to AI”, The Society of Authors.\n\nSorensen, E H and D A Williamson (1985), “Some evidence on the value of dividend discount models”, *Financial Analysts Journal* 4: 60-69.\n\nStacey, S and M Novik (2025), “How big is the stock market’s America bubble?”, *Financial Times.*\n\n*The Economist* (2025a), “AI is killing the web. Can anything save it?”.\n\n*The Economist* (2025b), “Welcome to the AI trough of disillusionment”.\n\nWeiss, T (2025), “The Decline of Stack Overflow”, Data Science with R.\n\nWidder, D G and M Hicks (2025), “Watching the Generative AI Hype Bubble Deflate”, Digital Life Initiative, Cornell University.","meta_canonical":null}

3. Robots.txt Check

Query:

Response:

4. Spam/Ban Check

Query:

Response:

5. Seen Status Check

ℹ️ Skipped - page is already crawled

📄

INDEXABLE

✅

CRAWLED

4 days ago

🤖

ROBOTS ALLOWED

Page Info Filters

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	PASS	`download_stamp > now() - 6 MONTH`	0.1 months ago
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

Page Details

Property	Value
URL	https://cepr.org/voxeu/columns/unpacking-us-tech-valuations-agnostic-assessment
Last Crawled	2026-04-04 04:06:04 (4 days ago)
First Indexed	2025-09-07 23:13:00 (7 months ago)
HTTP Status Code	200
Meta Title	Unpacking US tech valuations: An agnostic assessment \| CEPR
Meta Description	The recent performance of the largest US tech companies has raised concerns about the risk of a stock market bubble. Using a three-stage pricing model applied to the top ten US tech firms, this column finds that current market prices do not appear implausible given the underlying drivers of both recent and expected performance. The adoption of AI technologies will be a major engine of growth, which is already transforming markets and displacing jobs. However, risks will remain around the ability to identify reliable AI applications and the growing competition from abroad.
Meta Canonical	null
Boilerpipe Text	Innovations in artificial intelligence (AI) have fuelled substantial stock price gains for listed US technology companies. Nvidia, the most focused on AI technology, has been the first company to reach a $4 trillion capitalisation at the beginning of July, followed by Microsoft three weeks later. The top ten US tech companies 1 (henceforth, Mag10) now account for one-third and one-sixth of total US and global stock market capitalisations, respectively (Figure 1). 2 Alongside rising concentration, the elevated and growing valuations of US companies (as measured by their price/earnings ratios; Figure 2) have been a top-of-mind concern for financial analysts, prompting them to debate whether the US stock market is currently experiencing a bubble like the dot-com era of the late 1990s (Marks 2025, Stacey and Novik 2025, Duguid et al. 2025). In this column, we contribute to the debate by first presenting descriptive evidence on the valuations and fundamentals of the largest US tech companies. We then apply a simple three-stage pricing model to derive their market-implied expected profit growth rates. Finally, we discuss the plausibility of such expected growth rates. Descriptive evidence Price/earnings (P/E) ratios for the US stock market have been on an increasing trajectory for more than a decade, with a notable surge during the Covid period, and are indeed approaching the levels observed during the dot-com era (Figure 2). In contrast, t he P/E ratios of the largest tech companies remain well below the dot-com extremes, also because stock prices have not skyrocketed as spectacularly as they did in the late 1990s (Figures 3 and 4). By focusing on the Mag10, we find significant heterogeneity in their current and forward P/E ratios (between 20 and more than 600; Table 1). The smaller companies (Broadcom, Oracle, Palantir, and Tesla) are those that have the higher multiples. These are also the companies that have recently made – or are in the process of making – inroads into new, mostly AI-related businesses that have high growth potential and are expected to significantly change the level and composition of their earnings. 3 The larger companies, instead, have much lower multiples. This may reflect investors’ expectations that their profits are unlikely to experience spectacular growth rates, given their already huge size and reliance – at least in part – on fairly traditional and saturated markets. Finally, Google’s relatively low P/E ratio (around 20) seems to indicate that investors are not indiscriminately pouring money into AI companies: while Google has developed frontier and, in some cases, best-in-class AI models, it is not yet clear that its AI strategy is able to counter threats to its most lucrative business (Search), which might be displaced by the rapid change in users’ behaviour (LLM use instead of Internet navigation). Table 1 Mag10 market capitalisation and price/earnings multiples Source : LSEG. Note : Average values for 1-13 August 2025. This preliminary descriptive evidence suggests that investors are still significantly discriminating tech and AI-related firms by their idiosyncratic earnings prospects. Such discrimination tends to vanish during bubble episodes and other periods when sentiment and herd behaviour substantially shape valuations (Baker and Wurgler 2006, Chang et al. 2000). Evidence from an equilibrium dividend discount model A stock’s price should be equal to the present discounted value of its future dividends. Elevated P/E ratios may signal optimism about future profit and dividend growth, optimism that could prove either well-founded or misplaced, thereby exposing the market to the risk of abrupt corrections. In Albori et al. (forthcoming), we use an equilibrium dividend discount model (Molodovsky et al. 1965, Fuller and Hsia 1984, Sorensen and Williamson 1985) to compute the abnormal growth rates of earnings that would justify current equity valuations, that is, make them compatible with rational pricing in line with historical norms . Table 2 shows the model-implied growth rates, together with recent earnings growth rates and analysts’ expected growth rates, all inflation-adjusted. Table 2 Mag10 recent, expected, and model-implied growth in earnings per share (percent) Source : LSEG, our estimates. Note : Real (inflation-adjusted) annual growth rates in earnings per share (EPS). For Tesla, historical EPS growth is over the past four years; for Palantir, the past year. Growth expected by I/B/E/S analysts refers to the average growth rate over a period of three to five years. Model-implied growth rates are derived from P/E ratios as recorded on 28 July 2025. For the six largest companies in our sample, the average model-implied annual growth rate is 12% , with moderate dispersion. This indicates a significant deceleration from the 33% average growth observed over the past five years and is also more conservative than the 15% growth expected by analysts. For the remaining four companies, the mean model-implied growth rate is 41% , which compares with 39% in the previous five years and 17% expected by analysts. As discussed in the previous section, these four companies are radically different from the other six, in that they are much smaller and have recently entered fast-growing AI-related markets. Their implied growth rates are compatible with scenarios – far from being guaranteed, but also not utterly unrealistic – in which they manage to scale up their new business lines at a pace similar to that kept in recent years. In summary, current equity valuations for the top US tech companies are rational if one expects high, but not historically unusual growth rates for the coming years. Apart from historical comparisons, determining if these expected growth rates are realistic is challenging. This assessment relies heavily on subjective judgments about market developments, particularly the future demand for AI-related services and improvements in productivity and profitability driven by AI adoption. In the following section, we present information that may help inform these judgments. Factors affecting future earnings growth The main factors that contributed to the spectacular earnings growth of US tech companies in recent years (Figures 5 and 6) were: Strong increases in global spending on digital advertisements (by 15% annually on average between 2019 and 2024; Kemp 2025), often driven by the displacement of traditional advertisements (e.g. on TV and newspapers) and by the rise of e-commerce at the expense of brick-and-mortar stores. Amazon, Google, and Meta are market leaders in this space and have continued to expand their market shares (in 2024, they accounted for 51% of global ad sales; BestMediaInfo Bureau 2025). Increased corporate spending on both traditional and AI-related cloud services (up by 21% annually on average between 2019 and 2024; Slingerland 2025). Amazon, Google, and Microsoft are the main providers of these services, with a combined global market share of 63% (Brindley and Zhang 2025). Significant fractions of the hardware bought by these players to scale up their data centres is provided by NVIDIA and Broadcom. Still rapid global expansion of the number of smartphones (+5% average yearly growth between 2019 and 2024), coupled with the continued increase in the amount of time spent by users in the interaction with these devices (Howarth 2025). This has sustained the sales of phones (Apple, Google) and consumer-oriented cloud services (Apple, Google, Microsoft), and has helped fuel the previous two trends (spending on digital ads and cloud infrastructure). Gains in efficiency driven by economies of scale and AI adoption. For example, software development represents a significant fraction of the cost of offering the products and services mentioned above, but its cost tends to increase less than proportionally as the user base expands (the same piece of software serves more users). Moreover, AI has already facilitated the automation of labour-intensive tasks, such as content-moderation on social platforms (Meta). Reportedly, AI has also improved the economics of digital advertising, by allowing for a better targeting of users. According to the latest quarterly reports of the Mag10 (which on average showed still sustained growth), as well as to the expert sources cited above, none of these trends is expected to end abruptly in the near future, although some of them are expected to slow down. For example, smartphone adoption is expected to be sustained mostly by emerging markets, as advanced ones have already been saturated (Bellan 2025). Similarly, future growth in digital-advertisement sales may be hindered by the already large market shares held by the Mag10, although also in this case emerging markets represent a significant expansion opportunity. The growth prospects of cloud services (and related hardware) are probably the most controversial ones. While some analysts expect AI adoption to keep propping up cloud spending for the foreseeable future (Goldman Sachs 2024), others deem that current AI spending by corporations is bubbly and likely to slow down, as the economics of AI applications are often unproven (Widder and Hicks 2025). Whatever the stance on this issue, most analysts seem to agree that AI spending is the key factor that will determine whether tech companies are able to achieve the high growth rates envisaged by financial analysts and embedded in stock prices (e.g. our model-implied estimates). While taking a position in this debate is complex, we note that some recent trends seem to support the view that the demand for AI services will keep increasing at a brisk pace: As noted in a recent Economist article, the use of large language models (LLMs), such as ChatGPT, is quickly displacing Internet navigation (15% search traffic drop in the past year), as users find it more convenient to receive responses to their queries from an LLM rather than sifting through multiple websites (The Economist 2025a). This trend has just started, and it is likely to continue. The consequence is that a large portion of the revenues and profits previously made by millions of web-site owners and content creators will be shifted to cloud-service providers and hardware vendors (Godoy 2025, Weiss 2024), as on-the-fly content creation by LLMs requires enormous amounts of computational resources. At present, some of the major AI players still report difficulties in meeting these computational requirements, despite the huge investments in infrastructure made in previous years. Job destruction by generative AI technologies (not only LLMs, but also image and video generators) is reportedly gaining traction and economic significance not only in the website-ownership space, but also in adjacent occupations (ad creatives, illustrators, writers and copywriters, photographers, models, translators, moderators, etc.) (Bartholomew 2025). For example, according to a 2024 survey conducted by the Society of Authors (SOA Policy Team (2024), 26% of illustrators and 36% of translators report having already lost work due to generative AI, and three-quarters of them expect future income reductions. As explained in the previous point, this kind of displacement creates demand for the hardware and cloud services sold by the Mag10. AI technology is being increasingly adopted in the automotive sector (not only in autonomous vehicles, but also for driving assistance and infotainment). This trend – which will also significantly prop up the demand for cloud services, given the huge size of the automotive sector – has begun only recently, and it seems unlikely to abate, given that most auto-industry executives expect vehicles to become increasingly ‘software-defined’ (IBM 2025). These are just some examples of large markets where substantial AI adoption and, in some cases, the consequent displacement of jobs are not just a future possibility, but something that is already happening at scale. Mechanically, this adoption generates demand for compute, which is mostly offered by large US tech firms. Conclusions The largest US technology companies have recently recorded substantial increases in their valuations, prompting financial analysts and policymakers to express concerns about the risk of a stock market bubble. In this column, we present model-based evidence suggesting that the earnings growth rates required to justify current prices do not appear implausible given the underlying drivers of recent and expected performance. We also argue that the adoption of generative AI technologies will be a major engine of growth, and it is already transforming markets and displacing jobs at scale. Its rollout fuels rising demand for computing power, largely supplied by US tech companies, reinforcing their dominance. However, some cautions are in order. Some industries still struggle to identify reliable applications, sparking short-term disillusionment. This could temporarily curb corporate AI spending and drive a re-pricing of technology stocks (The Economist 2025b). Added to this, intensifying competition, particularly from China, threatens to erode big tech’s profit margins (Mims 2025). These factors could make the realisation of the growth rates implied by our model less certain. Authors’ note: The opinions expressed in this column are those of the authors and do not necessarily reflect the views of the Bank of Italy. References Albori, M, V Nispi Landi and M Taboga (forthcoming), “Is there a bubble in the US stock market? Evidence from an agnostic valuation procedure”, Bank of Italy Occasional Papers. Baker, M and J Wurgler (2006), “Investor sentiment and the cross‐section of stock returns”, The Journal of Finance 61: 1645-1680. Bartholomew, J (2025), “It’s happening fast’ – creative workers and professionals share their fears and hopes about the rise of AI”, The Guardian. Bellan, R (2025), “Apple: Pay attention to emerging markets, not falling China sales”, TechCrunch. BestMediaInfo Bureau (2025), “Google, Meta, Amazon’s combined share of global ad revenues hits 51% in 2024: Magna”. Brindley, R and Y Zhang (2025), “Worldwide cloud service spending to grow by 19% in 2025”, Canalys. Chang, E C, J W Cheng and A Khorana (2000), “An examination of herd behavior in equity markets: An international perspective”, Journal of Banking & Finance 24: 1651-1679. Duguid, K, G Steer, H Clarfelt and I Smith (2025), “Wall Street ‘euphoria’ sparks bubble warnings”, Financial Times. Fuller, R J and C C Hsia (1984), “A simplified common stock valuation model”, Financial Analysts Journal 40: 49-56. Godoy, J (2025), “Google's AI previews erode the internet, US edtech company says in lawsuit”, Reuters. Goldman Sachs (2024), “Cloud revenues poised to reach $2 trillion by 2030 amid AI rollout”, GS Artificial Intelligence. Howarth, J (2025), “How Many People Own Smartphones?”, Exploding Topics. IBM (2025), “Automotive in the AI era”, IBM Institute for Business Value. Kemp, S (2025), “Digital 2025: Global advertising trends”, DataReportal. Marks, H (2025), “Is the US stock market in bubble territory?”, Financial Times . Mims, C (2025), “Silicon Valley’s New Strategy: Move Slow and Build Things”, The Wall Street Journal. Molodovsky, N, C May and S Chottiner (1965), “Common stock valuation: Principles, tables and application”, Financial Analysts Journal 21: 104–123. Slingerland, C (2025), “90+ Cloud Computing Statistics: A 2025 Market Snapshot”, CloudZero. SOA Policy Team (2024), “SoA survey reveals a third of translators and quarter of illustrators losing work to AI”, The Society of Authors. Sorensen, E H and D A Williamson (1985), “Some evidence on the value of dividend discount models”, Financial Analysts Journal 4: 60-69. Stacey, S and M Novik (2025), “How big is the stock market’s America bubble?”, Financial Times. The Economist (2025a), “AI is killing the web. Can anything save it?”. The Economist (2025b), “Welcome to the AI trough of disillusionment”. Weiss, T (2025), “The Decline of Stack Overflow”, Data Science with R. Widder, D G and M Hicks (2025), “Watching the Generative AI Hype Bubble Deflate”, Digital Life Initiative, Cornell University.
Markdown	[Skip to main content](https://cepr.org/voxeu/columns/unpacking-us-tech-valuations-agnostic-assessment#main-content) Mobile menu Search - [VoxEU](https://cepr.org/voxeu) - [Search All Columns](https://cepr.org/voxeu/search-all-columns) - [VoxTalks](https://cepr.org/voxeu/voxtalks) - [VideoVox](https://cepr.org/audio-and-video/voxeu-videos) - [About VoxEU](https://cepr.org/voxeu/about-voxeu) - [Research](https://cepr.org/research) - [Programme Areas](https://cepr.org/research/programme-areas) - [Research Policy Networks (RPNs)](https://cepr.org/research/research-policy-networks-rpn) - [Research Projects and Networks](https://cepr.org/research/research-projects-and-networks) - [Research Policies](https://cepr.org/research/research-policies) - [Data Sets](https://cepr.org/research/data-set-items) - [Publications](https://cepr.org/publications) - [Publication Series](https://cepr.org/publications/publication-series) - [Books and Reports](https://cepr.org/publications/books-and-reports) - [Discussion Papers](https://cepr.org/publications/discussion-papers) - [Covid Economics Papers](https://cepr.org/publications/covid-economics-papers) - [Policy Papers](https://cepr.org/publications/policy-insights-policy-papers) - [Economic Policy Journal](https://cepr.org/publications/economic-policy-journal) - [Search All Publications](https://cepr.org/search?content_type%5B14%5D=14&type=publication) - [Archive](https://cepr.org/publications/archive) - [Events](https://cepr.org/events) - [Calls for Papers](https://cepr.org/events/open-calls-papers) - [Forthcoming Events](https://cepr.org/events/forthcoming-events) - [Past Events](https://cepr.org/events/past-events) - [Event Series](https://cepr.org/events/event-series) - [Audio and video](https://cepr.org/audio-and-video) - [Search all multimedia content](https://cepr.org/audio-and-video/search-all-multimedia) - [VoxTalks](https://cepr.org/voxeu/voxtalks) - [VideoVox](https://cepr.org/audio-and-video/voxeu-videos) - [Vox Webcasts](https://cepr.org/voxeu/vox-webcasts) - [Webinar Videos](https://cepr.org/audio-and-video/webinar-videos) - [About CEPR](https://cepr.org/about) - [People](https://cepr.org/about/people) - [News](https://cepr.org/about/news) - [Advisory Board](https://cepr.org/about/people/advisory-board) - [Press](https://cepr.org/about/press) - [Governance](https://cepr.org/about/governance) - [Support CEPR](https://cepr.org/about/join-cepr) - [Partners](https://cepr.org/partners) - [Jobs at CEPR](https://cepr.org/about/jobs-at-cepr) Themes Search Themes ## Themes - Key Themes - [Artificial Intelligence](https://cepr.org/themes/artificial-intelligence) - [Central Banking](https://cepr.org/themes/central-banking) - [Climate Change](https://cepr.org/themes/climate-change) - [Competition Policy](https://cepr.org/themes/competition-policy) - [Development & Growth](https://cepr.org/themes/development-growth) - [Economic History](https://cepr.org/themes/economic-history) - [Energy](https://cepr.org/themes/energy) - [EU Economic Architecture](https://cepr.org/themes/eu-economic-architecture "EU Economic Architecture") - [Inflation](https://cepr.org/themes/inflation) - [Political Economy](https://cepr.org/themes/political-economy "Political Economy") - [Poverty and income inequality](https://cepr.org/themes/poverty-and-income-inequality "Poverty and income inequality") - [Sustainable Finance](https://cepr.org/themes/sustainable-finance) - [Ukraine Initiative](https://cepr.org/themes/ukraine-initiative) - [Women in Economics](https://cepr.org/themes/women-economics) - Other Themes - [Business Cycles](https://cepr.org/themes/business-cycles) - [Covid 19](https://cepr.org/themes/covid-19) - [Exchange Rates](https://cepr.org/themes/exchange-rates) - [Finance & Fintech](https://cepr.org/themes/finance-and-fintech "Finance & Fintech") - [Health Economics](https://cepr.org/themes/health-economics) - [International Finance](https://cepr.org/themes/international-finance "International Finance") - [International Trade](https://cepr.org/themes/international-trade "International Trade") - [Labour Markets](https://cepr.org/themes/labour-markets) - [Migration](https://cepr.org/themes/migration "Migration") - [Monetary Policy](https://cepr.org/themes/monetary-policy "Monetary Policy") - [Productivity and Innovation](https://cepr.org/themes/productivity-and-innovation "Productivity and Innovation") - [Taxation](https://cepr.org/themes/taxation "Taxation") - Focus On - [The Economic Consequences of the War](https://cepr.org/debates/economic-consequences-war) - [Europe-Asia Initiative](https://cepr.org/europe-asia-initiative) - [Trump & Tariffs](https://cepr.org/voxeu/trump-and-tariffs) ## Main navigation - [VoxEU](https://cepr.org/voxeu) - [Search All Columns](https://cepr.org/voxeu/search-all-columns) - [VoxTalks](https://cepr.org/voxeu/voxtalks) - [VideoVox](https://cepr.org/audio-and-video/voxeu-videos) - [About VoxEU](https://cepr.org/voxeu/about-voxeu) - [Research](https://cepr.org/research) - [Publications](https://cepr.org/publications) - [Publication Series](https://cepr.org/publications/publication-series) - [Books and Reports](https://cepr.org/publications/books-and-reports) - [Discussion Papers](https://cepr.org/publications/discussion-papers) - [About CEPR Discussion Papers](https://cepr.org/publications/discussion-papers/about-cepr-discussion-papers) - [Discussion Paper Subscriptions](https://cepr.org/publications/discussion-papers/discussion-paper-subscriptions) - [Search Discussion Papers](https://cepr.org/publications/discussion-papers/search-discussion-papers) - [Covid Economics Papers](https://cepr.org/publications/covid-economics-papers) - [Policy Papers](https://cepr.org/publications/policy-insights-policy-papers) - [Economic Policy Journal](https://cepr.org/publications/economic-policy-journal) - [Search All Publications](https://cepr.org/search?content_type%5B14%5D=14&type=publication) - [Archive](https://cepr.org/publications/archive) - [CEPR Bulletin](https://cepr.org/publications/archive/cepr-bulletin) - [Events](https://cepr.org/events) - [Audio and video](https://cepr.org/audio-and-video) - [Search all multimedia content](https://cepr.org/audio-and-video/search-all-multimedia) - [VoxTalks](https://cepr.org/voxeu/voxtalks) - [VideoVox](https://cepr.org/audio-and-video/voxeu-videos) - [Vox Webcasts](https://cepr.org/voxeu/vox-webcasts) - [Webinar Videos](https://cepr.org/audio-and-video/webinar-videos) - [About CEPR](https://cepr.org/about) - [People](https://cepr.org/about/people) - [News](https://cepr.org/about/news) - [Advisory Board](https://cepr.org/about/people/advisory-board) - [Press](https://cepr.org/about/press) - [Governance](https://cepr.org/about/governance) - [Support CEPR](https://cepr.org/about/join-cepr) - [Partners](https://cepr.org/partners) - [Jobs at CEPR](https://cepr.org/about/jobs-at-cepr) ## Secondary Menu - [CEPR Offices](https://cepr.org/cepr-offices) ## User account menu - [Log in](https://cepr.org/moLogin) ## Secondary Menu - [CEPR Offices](https://cepr.org/cepr-offices) ## User account menu - [Log in](https://cepr.org/moLogin) ## Themes - Key Themes - [Artificial Intelligence](https://cepr.org/themes/artificial-intelligence) - [Central Banking](https://cepr.org/themes/central-banking) - [Climate Change](https://cepr.org/themes/climate-change) - [Competition Policy](https://cepr.org/themes/competition-policy) - [Development & Growth](https://cepr.org/themes/development-growth) - [Economic History](https://cepr.org/themes/economic-history) - [Energy](https://cepr.org/themes/energy) - [EU Economic Architecture](https://cepr.org/themes/eu-economic-architecture "EU Economic Architecture") - [Inflation](https://cepr.org/themes/inflation) - [Political Economy](https://cepr.org/themes/political-economy "Political Economy") - [Poverty and income inequality](https://cepr.org/themes/poverty-and-income-inequality "Poverty and income inequality") - [Sustainable Finance](https://cepr.org/themes/sustainable-finance) - [Ukraine Initiative](https://cepr.org/themes/ukraine-initiative) - [Women in Economics](https://cepr.org/themes/women-economics) - Other Themes - [Business Cycles](https://cepr.org/themes/business-cycles) - [Covid 19](https://cepr.org/themes/covid-19) - [Exchange Rates](https://cepr.org/themes/exchange-rates) - [Finance & Fintech](https://cepr.org/themes/finance-and-fintech "Finance & Fintech") - [Health Economics](https://cepr.org/themes/health-economics) - [International Finance](https://cepr.org/themes/international-finance "International Finance") - [International Trade](https://cepr.org/themes/international-trade "International Trade") - [Labour Markets](https://cepr.org/themes/labour-markets) - [Migration](https://cepr.org/themes/migration "Migration") - [Monetary Policy](https://cepr.org/themes/monetary-policy "Monetary Policy") - [Productivity and Innovation](https://cepr.org/themes/productivity-and-innovation "Productivity and Innovation") - [Taxation](https://cepr.org/themes/taxation "Taxation") - Focus On - [The Economic Consequences of the War](https://cepr.org/debates/economic-consequences-war) - [Europe-Asia Initiative](https://cepr.org/europe-asia-initiative) - [Trump & Tariffs](https://cepr.org/voxeu/trump-and-tariffs) Search the site 1. / [VoxEU](https://cepr.org/voxeu) 2. / [Search All Columns](https://cepr.org/voxeu/search-all-columns) In this section - [Search All Columns](https://cepr.org/voxeu/search-all-columns) - [VoxTalks](https://cepr.org/voxeu/voxtalks) - [VoxEU Videos](https://cepr.org/audio-and-video/voxeu-videos) - [About VoxEU](https://cepr.org/voxeu/about-voxeu) ![stock market heat map showing major U.S. tech companies](https://cepr.org/sites/default/files/styles/16_9_small/public/2025-09/AdobeStock_1322385575_Editorial_Use_Only.jpeg?itok=CYyv4Xmq) VoxEU Column Financial Markets # Unpacking US tech valuations: An agnostic assessment - [Marco Albori](https://cepr.org/about/people/marco-albori) - [Valerio Nispi Landi](https://cepr.org/about/people/valerio-nispi-landi) - [Marco Taboga](https://cepr.org/about/people/marco-taboga) - / 8 Sep 2025 The recent performance of the largest US tech companies has raised concerns about the risk of a stock market bubble. Using a three-stage pricing model applied to the top ten US tech firms, this column finds that current market prices do not appear implausible given the underlying drivers of both recent and expected performance. The adoption of AI technologies will be a major engine of growth, which is already transforming markets and displacing jobs. However, risks will remain around the ability to identify reliable AI applications and the growing competition from abroad. #### Authors [![](https://cepr.org/sites/default/files/styles/portrait_small/public/profile-photos/208527-208445_marco_albori_1dbdb114a574a3b39687513a61cf9f6c_29de97016cb8c7dca7bde57b9ac9e5b5.jpg?itok=cJ6TB4Nc)](https://cepr.org/about/people/marco-albori "Go to profile of Marco Albori") ### [Marco Albori](https://cepr.org/about/people/marco-albori "Go to profile of Marco Albori") [![](https://cepr.org/sites/default/files/styles/portrait_small/public/profile-photos/184576-184576_valerio_nispi_landi_b9274da1487b4ec37f0ba20eee8706a4_ad68f9066f1efe5dec3325bbd7aec791.jpeg?itok=DmINHy8K)](https://cepr.org/about/people/valerio-nispi-landi "Go to profile of Valerio Nispi Landi") ### [Valerio Nispi Landi](https://cepr.org/about/people/valerio-nispi-landi "Go to profile of Valerio Nispi Landi") [![](https://cepr.org/sites/default/files/styles/portrait_small/public/profile-photos/222560-taboga_77dcf1b82938f337d3f3b788c6b13d11.jpg?itok=GpzV-KEU)](https://cepr.org/about/people/marco-taboga "Go to profile of Marco Taboga") ### [Marco Taboga](https://cepr.org/about/people/marco-taboga "Go to profile of Marco Taboga") #### Share - [Twitter](https://twitter.com/intent/tweet?text=Unpacking%20US%20tech%20valuations%3A%20An%20agnostic%20assessment&url=https%3A%2F%2Fcepr.org%2Fvoxeu%2Fcolumns%2Funpacking-us-tech-valuations-agnostic-assessment&via=cepr_org&hashtags= "Share via Twitter") - [Facebook](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Fcepr.org%2Fvoxeu%2Fcolumns%2Funpacking-us-tech-valuations-agnostic-assessment "Share via Facebook") - [LinkedIn](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Fcepr.org%2Fvoxeu%2Fcolumns%2Funpacking-us-tech-valuations-agnostic-assessment&title=Unpacking%20US%20tech%20valuations%3A%20An%20agnostic%20assessment&source=CEPR&summary=The%20recent%20performance%20of%20the%20largest%20US%20tech%20companies%20has%20raised%20concerns%20about%20the%20risk%20of%20a%20stock%20market%20bubble.%20Using%20a%20three-stage%20pricing%20model%20applied%20to%20the%20top%20ten%20US%20tech%20firms,%20this%20column%20finds%20that%20current%20market%20prices%20do%20not%20appear%20implausible%20given%20the%20underlying%20drivers%20of%20both%20recent%20and%20expected%20performance.%20The%20adoption%20of%20AI%20technologies%20will%20be%20a%20major%20engine%20of%20growth,%20which%20is%20already%20transforming%20markets%20and%20displacing%20jobs.%20However,%20risks%20will%20remain%20around%20the%20ability%20to%20identify%20reliable%20AI%20applications%20and%20the%20growing%20competition%20from%20abroad. "Share via LinkedIn") - [Bluesky](https://bsky.app/intent/compose?text=Unpacking%20US%20tech%20valuations%3A%20An%20agnostic%20assessment%20https%3A%2F%2Fcepr.org%2Fvoxeu%2Fcolumns%2Funpacking-us-tech-valuations-agnostic-assessment "Share via Bluesky") Innovations in artificial intelligence (AI) have fuelled substantial stock price gains for listed US technology companies. Nvidia, the most focused on AI technology, has been the first company to reach a \$4 trillion capitalisation at the beginning of July, followed by Microsoft three weeks later. The top ten US tech companies [1](https://cepr.org/voxeu/columns/unpacking-us-tech-valuations-agnostic-assessment#footnote1_9joowpb "Scroll down to this footnote") (henceforth, Mag10) now account for one-third and one-sixth of total US and global stock market capitalisations, respectively (Figure 1). [2](https://cepr.org/voxeu/columns/unpacking-us-tech-valuations-agnostic-assessment#footnote2_jfw2q0b "Scroll down to this footnote") Alongside rising concentration, the elevated and growing valuations of US companies (as measured by their price/earnings ratios; Figure 2) have been a top-of-mind concern for financial analysts, prompting them to debate whether the US stock market is currently experiencing a bubble like the dot-com era of the late 1990s (Marks 2025, Stacey and Novik 2025, Duguid et al. 2025). In this column, we contribute to the debate by first presenting descriptive evidence on the valuations and fundamentals of the largest US tech companies. We then apply a simple three-stage pricing model to derive their market-implied expected profit growth rates. Finally, we discuss the plausibility of such expected growth rates. ![Figures 1 and 2](https://cepr.org/sites/default/files/styles/flexible_wysiwyg/public/2025-09/landifig1.png?itok=RfVgN7vX) ![Figures 1 and 2](https://cepr.org/sites/default/files/styles/popup_small/public/2025-09/landifig1.png?itok=9p1dqIiY) ## Descriptive evidence Price/earnings (P/E) ratios for the US stock market have been on an increasing trajectory for more than a decade, with a notable surge during the Covid period, and are indeed approaching the levels observed during the dot-com era (Figure 2). In contrast, the P/E ratios of the largest tech companies remain well below the dot-com extremes, also because stock prices have not skyrocketed as spectacularly as they did in the late 1990s (Figures 3 and 4). ![Figures 3 and 4](https://cepr.org/sites/default/files/styles/flexible_wysiwyg/public/2025-09/landifig2.png?itok=tM_MzWtg) ![Figures 3 and 4](https://cepr.org/sites/default/files/styles/popup_small/public/2025-09/landifig2.png?itok=7gNCmE1O) By focusing on the Mag10, we find significant heterogeneity in their current and forward P/E ratios (between 20 and more than 600; Table 1). The smaller companies (Broadcom, Oracle, Palantir, and Tesla) are those that have the higher multiples. These are also the companies that have recently made – or are in the process of making – inroads into new, mostly AI-related businesses that have high growth potential and are expected to significantly change the level and composition of their earnings. [3](https://cepr.org/voxeu/columns/unpacking-us-tech-valuations-agnostic-assessment#footnote3_ga9noy0 "Scroll down to this footnote") The larger companies, instead, have much lower multiples. This may reflect investors’ expectations that their profits are unlikely to experience spectacular growth rates, given their already huge size and reliance – at least in part – on fairly traditional and saturated markets. Finally, Google’s relatively low P/E ratio (around 20) seems to indicate that investors are not indiscriminately pouring money into AI companies: while Google has developed frontier and, in some cases, best-in-class AI models, it is not yet clear that its AI strategy is able to counter threats to its most lucrative business (Search), which might be displaced by the rapid change in users’ behaviour (LLM use instead of Internet navigation). Table 1 Mag10 market capitalisation and price/earnings multiples ![Table 1 Mag10 market capitalisation and price/earnings multiples](https://cepr.org/sites/default/files/styles/flexible_wysiwyg/public/2025-09/landitable1.png?itok=IsiNg2NM) ![Table 1 Mag10 market capitalisation and price/earnings multiples](https://cepr.org/sites/default/files/styles/popup_small/public/2025-09/landitable1.png?itok=P_a0sFCA) ###### Source: LSEG. Note: Average values for 1-13 August 2025. This preliminary descriptive evidence suggests that investors are still significantly discriminating tech and AI-related firms by their idiosyncratic earnings prospects. Such discrimination tends to vanish during bubble episodes and other periods when sentiment and herd behaviour substantially shape valuations (Baker and Wurgler 2006, Chang et al. 2000). ## Evidence from an equilibrium dividend discount model A stock’s price should be equal to the present discounted value of its future dividends. Elevated P/E ratios may signal optimism about future profit and dividend growth, optimism that could prove either well-founded or misplaced, thereby exposing the market to the risk of abrupt corrections. In Albori et al. (forthcoming), we use an equilibrium dividend discount model (Molodovsky et al. 1965, Fuller and Hsia 1984, Sorensen and Williamson 1985) to compute the abnormal growth rates of earnings that would justify current equity valuations, that is, make them compatible with rational pricing in line with historical norms. Table 2 shows the model-implied growth rates, together with recent earnings growth rates and analysts’ expected growth rates, all inflation-adjusted. Table 2 Mag10 recent, expected, and model-implied growth in earnings per share (percent) ![Table 2 Mag10 recent, expected, and model-implied growth in earnings per share](https://cepr.org/sites/default/files/styles/flexible_wysiwyg/public/2025-09/landitable2.png?itok=M2uwey4y) ![Table 2 Mag10 recent, expected, and model-implied growth in earnings per share](https://cepr.org/sites/default/files/styles/popup_small/public/2025-09/landitable2.png?itok=ZZ4ojHK9) ###### Source: LSEG, our estimates. Note: Real (inflation-adjusted) annual growth rates in earnings per share (EPS). For Tesla, historical EPS growth is over the past four years; for Palantir, the past year. Growth expected by I/B/E/S analysts refers to the average growth rate over a period of three to five years. Model-implied growth rates are derived from P/E ratios as recorded on 28 July 2025. For the six largest companies in our sample, the average model-implied annual growth rate is 12%, with moderate dispersion. This indicates a significant deceleration from the 33% average growth observed over the past five years and is also more conservative than the 15% growth expected by analysts. For the remaining four companies, the mean model-implied growth rate is 41%, which compares with 39% in the previous five years and 17% expected by analysts. As discussed in the previous section, these four companies are radically different from the other six, in that they are much smaller and have recently entered fast-growing AI-related markets. Their implied growth rates are compatible with scenarios – far from being guaranteed, but also not utterly unrealistic – in which they manage to scale up their new business lines at a pace similar to that kept in recent years. In summary, current equity valuations for the top US tech companies are rational if one expects high, but not historically unusual growth rates for the coming years. Apart from historical comparisons, determining if these expected growth rates are realistic is challenging. This assessment relies heavily on subjective judgments about market developments, particularly the future demand for AI-related services and improvements in productivity and profitability driven by AI adoption. In the following section, we present information that may help inform these judgments. ## Factors affecting future earnings growth The main factors that contributed to the spectacular earnings growth of US tech companies in recent years (Figures 5 and 6) were: - Strong increases in global spending on digital advertisements (by 15% annually on average between 2019 and 2024; Kemp 2025), often driven by the displacement of traditional advertisements (e.g. on TV and newspapers) and by the rise of e-commerce at the expense of brick-and-mortar stores. Amazon, Google, and Meta are market leaders in this space and have continued to expand their market shares (in 2024, they accounted for 51% of global ad sales; BestMediaInfo Bureau 2025). - Increased corporate spending on both traditional and AI-related cloud services (up by 21% annually on average between 2019 and 2024; Slingerland 2025). Amazon, Google, and Microsoft are the main providers of these services, with a combined global market share of 63% (Brindley and Zhang 2025). Significant fractions of the hardware bought by these players to scale up their data centres is provided by NVIDIA and Broadcom. - Still rapid global expansion of the number of smartphones (+5% average yearly growth between 2019 and 2024), coupled with the continued increase in the amount of time spent by users in the interaction with these devices (Howarth 2025). This has sustained the sales of phones (Apple, Google) and consumer-oriented cloud services (Apple, Google, Microsoft), and has helped fuel the previous two trends (spending on digital ads and cloud infrastructure). - Gains in efficiency driven by economies of scale and AI adoption. For example, software development represents a significant fraction of the cost of offering the products and services mentioned above, but its cost tends to increase less than proportionally as the user base expands (the same piece of software serves more users). Moreover, AI has already facilitated the automation of labour-intensive tasks, such as content-moderation on social platforms (Meta). Reportedly, AI has also improved the economics of digital advertising, by allowing for a better targeting of users. ![Figures 5 and 6](https://cepr.org/sites/default/files/styles/flexible_wysiwyg/public/2025-09/landifig3.png?itok=64a1MZbq) ![Figures 5 and 6](https://cepr.org/sites/default/files/styles/popup_small/public/2025-09/landifig3.png?itok=0AQdo6oC) According to the latest quarterly reports of the Mag10 (which on average showed still sustained growth), as well as to the expert sources cited above, none of these trends is expected to end abruptly in the near future, although some of them are expected to slow down. For example, smartphone adoption is expected to be sustained mostly by emerging markets, as advanced ones have already been saturated (Bellan 2025). Similarly, future growth in digital-advertisement sales may be hindered by the already large market shares held by the Mag10, although also in this case emerging markets represent a significant expansion opportunity. The growth prospects of cloud services (and related hardware) are probably the most controversial ones. While some analysts expect AI adoption to keep propping up cloud spending for the foreseeable future (Goldman Sachs 2024), others deem that current AI spending by corporations is bubbly and likely to slow down, as the economics of AI applications are often unproven (Widder and Hicks 2025). Whatever the stance on this issue, most analysts seem to agree that AI spending is the key factor that will determine whether tech companies are able to achieve the high growth rates envisaged by financial analysts and embedded in stock prices (e.g. our model-implied estimates). While taking a position in this debate is complex, we note that some recent trends seem to support the view that the demand for AI services will keep increasing at a brisk pace: - As noted in a recent Economist article, the use of large language models (LLMs), such as ChatGPT, is quickly displacing Internet navigation (15% search traffic drop in the past year), as users find it more convenient to receive responses to their queries from an LLM rather than sifting through multiple websites (The Economist 2025a). This trend has just started, and it is likely to continue. The consequence is that a large portion of the revenues and profits previously made by millions of web-site owners and content creators will be shifted to cloud-service providers and hardware vendors (Godoy 2025, Weiss 2024), as on-the-fly content creation by LLMs requires enormous amounts of computational resources. At present, some of the major AI players still report difficulties in meeting these computational requirements, despite the huge investments in infrastructure made in previous years. - Job destruction by generative AI technologies (not only LLMs, but also image and video generators) is reportedly gaining traction and economic significance not only in the website-ownership space, but also in adjacent occupations (ad creatives, illustrators, writers and copywriters, photographers, models, translators, moderators, etc.) (Bartholomew 2025). For example, according to a 2024 survey conducted by the Society of Authors (SOA Policy Team (2024), 26% of illustrators and 36% of translators report having already lost work due to generative AI, and three-quarters of them expect future income reductions. As explained in the previous point, this kind of displacement creates demand for the hardware and cloud services sold by the Mag10. - AI technology is being increasingly adopted in the automotive sector (not only in autonomous vehicles, but also for driving assistance and infotainment). This trend – which will also significantly prop up the demand for cloud services, given the huge size of the automotive sector – has begun only recently, and it seems unlikely to abate, given that most auto-industry executives expect vehicles to become increasingly ‘software-defined’ (IBM 2025). These are just some examples of large markets where substantial AI adoption and, in some cases, the consequent displacement of jobs are not just a future possibility, but something that is already happening at scale. Mechanically, this adoption generates demand for compute, which is mostly offered by large US tech firms. ## Conclusions The largest US technology companies have recently recorded substantial increases in their valuations, prompting financial analysts and policymakers to express concerns about the risk of a stock market bubble. In this column, we present model-based evidence suggesting that the earnings growth rates required to justify current prices do not appear implausible given the underlying drivers of recent and expected performance. We also argue that the adoption of generative AI technologies will be a major engine of growth, and it is already transforming markets and displacing jobs at scale. Its rollout fuels rising demand for computing power, largely supplied by US tech companies, reinforcing their dominance. However, some cautions are in order. Some industries still struggle to identify reliable applications, sparking short-term disillusionment. This could temporarily curb corporate AI spending and drive a re-pricing of technology stocks (The Economist 2025b). Added to this, intensifying competition, particularly from China, threatens to erode big tech’s profit margins (Mims 2025). These factors could make the realisation of the growth rates implied by our model less certain. Authors’ note: The opinions expressed in this column are those of the authors and do not necessarily reflect the views of the Bank of Italy. ## References Albori, M, V Nispi Landi and M Taboga (forthcoming), “Is there a bubble in the US stock market? Evidence from an agnostic valuation procedure”, Bank of Italy Occasional Papers. Baker, M and J Wurgler (2006), “Investor sentiment and the cross‐section of stock returns”, The Journal of Finance 61: 1645-1680. Bartholomew, J (2025), “It’s happening fast’ – creative workers and professionals share their fears and hopes about the rise of AI”, The Guardian. Bellan, R (2025), “Apple: Pay attention to emerging markets, not falling China sales”, TechCrunch. BestMediaInfo Bureau (2025), “Google, Meta, Amazon’s combined share of global ad revenues hits 51% in 2024: Magna”. Brindley, R and Y Zhang (2025), “Worldwide cloud service spending to grow by 19% in 2025”, Canalys. Chang, E C, J W Cheng and A Khorana (2000), “An examination of herd behavior in equity markets: An international perspective”, Journal of Banking & Finance 24: 1651-1679. Duguid, K, G Steer, H Clarfelt and I Smith (2025), “Wall Street ‘euphoria’ sparks bubble warnings”, Financial Times. Fuller, R J and C C Hsia (1984), “A simplified common stock valuation model”, Financial Analysts Journal 40: 49-56. Godoy, J (2025), “Google's AI previews erode the internet, US edtech company says in lawsuit”, Reuters. Goldman Sachs (2024), “Cloud revenues poised to reach \$2 trillion by 2030 amid AI rollout”, GS Artificial Intelligence. Howarth, J (2025), “How Many People Own Smartphones?”, Exploding Topics. IBM (2025), “Automotive in the AI era”, IBM Institute for Business Value. Kemp, S (2025), “Digital 2025: Global advertising trends”, DataReportal. Marks, H (2025), “Is the US stock market in bubble territory?”, Financial Times. Mims, C (2025), “Silicon Valley’s New Strategy: Move Slow and Build Things”, The Wall Street Journal. Molodovsky, N, C May and S Chottiner (1965), “Common stock valuation: Principles, tables and application”, Financial Analysts Journal 21: 104–123. Slingerland, C (2025), “90+ Cloud Computing Statistics: A 2025 Market Snapshot”, CloudZero. SOA Policy Team (2024), “SoA survey reveals a third of translators and quarter of illustrators losing work to AI”, The Society of Authors. Sorensen, E H and D A Williamson (1985), “Some evidence on the value of dividend discount models”, Financial Analysts Journal 4: 60-69. Stacey, S and M Novik (2025), “How big is the stock market’s America bubble?”, Financial Times. The Economist (2025a), “AI is killing the web. Can anything save it?”. The Economist (2025b), “Welcome to the AI trough of disillusionment”. Weiss, T (2025), “The Decline of Stack Overflow”, Data Science with R. Widder, D G and M Hicks (2025), “Watching the Generative AI Hype Bubble Deflate”, Digital Life Initiative, Cornell University. #### Footnotes 1. Alphabet (Google), Amazon, Apple, Broadcom, Meta, Microsoft, Nvidia, Oracle, Palantir and Tesla. 2. Based respectively on Datastream’s US Total Market Index (including about one thousand constituent stocks) and World Total Market Index (including more than 7000). 3. Broadcom has recently become a key supplier of AI chips that offer an alternative to NVIDIA’s silicon. Oracle, traditionally a supplier of database software, has successfully scaled up its cloud data centre business, which is deemed able to compete with those of major cloud-services vendors such as Amazon. Palantir, which previously provided database-integration services mostly to defence and law-enforcement contractors, has significantly expanded its offerings with AI services that appeal to large corporations. Tesla is continuing to develop autonomous driving and a humanoid robot that – in case of commercialisation – could open huge markets. #### Authors [![](https://cepr.org/sites/default/files/styles/portrait_small/public/profile-photos/208527-208445_marco_albori_1dbdb114a574a3b39687513a61cf9f6c_29de97016cb8c7dca7bde57b9ac9e5b5.jpg?itok=cJ6TB4Nc)](https://cepr.org/about/people/marco-albori "Go to profile of Marco Albori") ### [Marco Albori](https://cepr.org/about/people/marco-albori "Go to profile of Marco Albori") Economist Bank Of Italy [![](https://cepr.org/sites/default/files/styles/portrait_small/public/profile-photos/184576-184576_valerio_nispi_landi_b9274da1487b4ec37f0ba20eee8706a4_ad68f9066f1efe5dec3325bbd7aec791.jpeg?itok=DmINHy8K)](https://cepr.org/about/people/valerio-nispi-landi "Go to profile of Valerio Nispi Landi") ### [Valerio Nispi Landi](https://cepr.org/about/people/valerio-nispi-landi "Go to profile of Valerio Nispi Landi") Economist Bank Of Italy [![](https://cepr.org/sites/default/files/styles/portrait_small/public/profile-photos/222560-taboga_77dcf1b82938f337d3f3b788c6b13d11.jpg?itok=GpzV-KEU)](https://cepr.org/about/people/marco-taboga "Go to profile of Marco Taboga") ### [Marco Taboga](https://cepr.org/about/people/marco-taboga "Go to profile of Marco Taboga") Head of the International Finance and Commodity Markets Division Bank Of Italy #### Themes - [Financial Markets](https://cepr.org/themes/financial-markets) #### Keywords - [tech bubble](https://cepr.org/search?keyword%5B365270%5D=365270) #### Share - [Twitter](https://twitter.com/intent/tweet?text=Unpacking%20US%20tech%20valuations%3A%20An%20agnostic%20assessment&url=https%3A%2F%2Fcepr.org%2Fvoxeu%2Fcolumns%2Funpacking-us-tech-valuations-agnostic-assessment&via=cepr_org&hashtags= "Share via Twitter") - [Facebook](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Fcepr.org%2Fvoxeu%2Fcolumns%2Funpacking-us-tech-valuations-agnostic-assessment "Share via Facebook") - [LinkedIn](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Fcepr.org%2Fvoxeu%2Fcolumns%2Funpacking-us-tech-valuations-agnostic-assessment&title=Unpacking%20US%20tech%20valuations%3A%20An%20agnostic%20assessment&source=CEPR&summary=The%20recent%20performance%20of%20the%20largest%20US%20tech%20companies%20has%20raised%20concerns%20about%20the%20risk%20of%20a%20stock%20market%20bubble.%20Using%20a%20three-stage%20pricing%20model%20applied%20to%20the%20top%20ten%20US%20tech%20firms,%20this%20column%20finds%20that%20current%20market%20prices%20do%20not%20appear%20implausible%20given%20the%20underlying%20drivers%20of%20both%20recent%20and%20expected%20performance.%20The%20adoption%20of%20AI%20technologies%20will%20be%20a%20major%20engine%20of%20growth,%20which%20is%20already%20transforming%20markets%20and%20displacing%20jobs.%20However,%20risks%20will%20remain%20around%20the%20ability%20to%20identify%20reliable%20AI%20applications%20and%20the%20growing%20competition%20from%20abroad. "Share via LinkedIn") - [Bluesky](https://bsky.app/intent/compose?text=Unpacking%20US%20tech%20valuations%3A%20An%20agnostic%20assessment%20https%3A%2F%2Fcepr.org%2Fvoxeu%2Fcolumns%2Funpacking-us-tech-valuations-agnostic-assessment "Share via Bluesky") [![person talking with robot](https://cepr.org/sites/default/files/styles/16_9_small/public/2023-06/AdobeStock_571280527.jpeg?itok=JJhMDCms)](https://cepr.org/voxeu/columns/generative-ai-and-firm-valuation "Generative AI and firm valuation") VoxEU Column ### [Generative AI and firm valuation](https://cepr.org/voxeu/columns/generative-ai-and-firm-valuation) - [Andrea Eisfeldt](https://cepr.org/about/people/andrea-eisfeldt) - [Gregor Schubert](https://cepr.org/about/people/gregor-schubert-0) - [Miao Ben Zhang](https://cepr.org/about/people/miao-ben-zhang) 4 Jun 2023 - [Labour Markets](https://cepr.org/themes/labour-markets "Go to the Labour Markets page") - [Productivity and Innovation](https://cepr.org/themes/productivity-and-innovation "Go to the Productivity and Innovation page") [![AdobeStock\_250694777.jpeg](https://cepr.org/sites/default/files/styles/16_9_small/public/voxeu-cover-image/AdobeStock_250694777.jpeg?itok=PQ4Dc5YL)](https://cepr.org/voxeu/columns/beware-tech-bubbles-long-term-earnings-dot-com-bubble-generation "Beware of tech bubbles: Long-term earnings of the dot-com bubble generation") VoxEU Column ### [Beware of tech bubbles: Long-term earnings of the dot-com bubble generation](https://cepr.org/voxeu/columns/beware-tech-bubbles-long-term-earnings-dot-com-bubble-generation) - [Adrien Matray](https://cepr.org/about/people/adrien-matray) - [Johan Hombert](https://cepr.org/about/people/johan-hombert) 5 Jan 2020 - [Labour Markets](https://cepr.org/themes/labour-markets "Go to the Labour Markets page") [![Microchip with brain illustration ](https://cepr.org/sites/default/files/styles/16_9_small/public/2023-06/AdobeStock_159300418.jpeg?itok=qpEqAnT7)](https://cepr.org/voxeu/columns/impact-artificial-intelligence-growth-and-employment "The impact of artificial intelligence on growth and employment") VoxEU Column ### [The impact of artificial intelligence on growth and employment](https://cepr.org/voxeu/columns/impact-artificial-intelligence-growth-and-employment) - [Ethan Ilzetzki](https://cepr.org/about/people/ethan-ilzetzki) - [Suryaansh Jain](https://cepr.org/about/people/suryaansh-jain) 20 Jun 2023 - [Labour Markets](https://cepr.org/themes/labour-markets "Go to the Labour Markets page") - [Productivity and Innovation](https://cepr.org/themes/productivity-and-innovation "Go to the Productivity and Innovation page") - Themes & Current Issues - [Artificial Intelligence](https://cepr.org/themes/artificial-intelligence) - [Business Cycles](https://cepr.org/themes/business-cycles) - [Central Banking](https://cepr.org/themes/central-banking) - [Climate Change](https://cepr.org/themes/climate-change) - [Competition Policy](https://cepr.org/themes/competition-policy) - [COVID-19](https://cepr.org/themes/covid-19) - [Development & Growth](https://cepr.org/themes/development-growth) - [Economic history](https://cepr.org/themes/economic-history) - [Energy](https://cepr.org/themes/energy) - [EU Economic Architecture](https://cepr.org/themes/eu-economic-architecture) - [Exchange Rates](https://cepr.org/themes/exchange-rates) - [Finance and Fintech](https://cepr.org/themes/finance-and-fintech) - [Financial Markets](https://cepr.org/themes/financial-markets) - [Financial Regulation and Banking](https://cepr.org/themes/financial-regulation-and-banking) - [Health Economics](https://cepr.org/themes/health-economics) - [Inflation](https://cepr.org/themes/inflation) - [International Finance](https://cepr.org/themes/international-finance) - [International trade](https://cepr.org/themes/international-trade) - [Labour Markets](https://cepr.org/themes/labour-markets) - [Migration](https://cepr.org/themes/migration) - [Monetary Policy](https://cepr.org/themes/monetary-policy) - [Political Economy](https://cepr.org/themes/political-economy) - [Poverty and Income Inequality](https://cepr.org/themes/poverty-and-income-inequality) - [Productivity and Innovation](https://cepr.org/themes/productivity-and-innovation) - [Taxation](https://cepr.org/themes/taxation) - [Ukraine Initiative](https://cepr.org/themes/ukraine-initiative) - [Women in Economics](https://cepr.org/themes/women-economics) ###### Sign up to our newsletter ###### follow us Copyright 2026 CEPR /[designbysoapbox.com](https://www.designbysoapbox.com/) - [Contact Us](https://cepr.org/contact-us "How to contact us") - [Cookies](https://cepr.org/cookies) - [Privacy Policy](https://cepr.org/privacy-policy) - [RSS Feeds](https://cepr.org/rss-feeds)
Readable Markdown	Innovations in artificial intelligence (AI) have fuelled substantial stock price gains for listed US technology companies. Nvidia, the most focused on AI technology, has been the first company to reach a \$4 trillion capitalisation at the beginning of July, followed by Microsoft three weeks later. The top ten US tech companies [1](https://cepr.org/voxeu/columns/unpacking-us-tech-valuations-agnostic-assessment#footnote1_9joowpb "Scroll down to this footnote") (henceforth, Mag10) now account for one-third and one-sixth of total US and global stock market capitalisations, respectively (Figure 1). [2](https://cepr.org/voxeu/columns/unpacking-us-tech-valuations-agnostic-assessment#footnote2_jfw2q0b "Scroll down to this footnote") Alongside rising concentration, the elevated and growing valuations of US companies (as measured by their price/earnings ratios; Figure 2) have been a top-of-mind concern for financial analysts, prompting them to debate whether the US stock market is currently experiencing a bubble like the dot-com era of the late 1990s (Marks 2025, Stacey and Novik 2025, Duguid et al. 2025). In this column, we contribute to the debate by first presenting descriptive evidence on the valuations and fundamentals of the largest US tech companies. We then apply a simple three-stage pricing model to derive their market-implied expected profit growth rates. Finally, we discuss the plausibility of such expected growth rates. ## Descriptive evidence Price/earnings (P/E) ratios for the US stock market have been on an increasing trajectory for more than a decade, with a notable surge during the Covid period, and are indeed approaching the levels observed during the dot-com era (Figure 2). In contrast, the P/E ratios of the largest tech companies remain well below the dot-com extremes, also because stock prices have not skyrocketed as spectacularly as they did in the late 1990s (Figures 3 and 4). By focusing on the Mag10, we find significant heterogeneity in their current and forward P/E ratios (between 20 and more than 600; Table 1). The smaller companies (Broadcom, Oracle, Palantir, and Tesla) are those that have the higher multiples. These are also the companies that have recently made – or are in the process of making – inroads into new, mostly AI-related businesses that have high growth potential and are expected to significantly change the level and composition of their earnings. [3](https://cepr.org/voxeu/columns/unpacking-us-tech-valuations-agnostic-assessment#footnote3_ga9noy0 "Scroll down to this footnote") The larger companies, instead, have much lower multiples. This may reflect investors’ expectations that their profits are unlikely to experience spectacular growth rates, given their already huge size and reliance – at least in part – on fairly traditional and saturated markets. Finally, Google’s relatively low P/E ratio (around 20) seems to indicate that investors are not indiscriminately pouring money into AI companies: while Google has developed frontier and, in some cases, best-in-class AI models, it is not yet clear that its AI strategy is able to counter threats to its most lucrative business (Search), which might be displaced by the rapid change in users’ behaviour (LLM use instead of Internet navigation). Table 1 Mag10 market capitalisation and price/earnings multiples ###### Source: LSEG. Note: Average values for 1-13 August 2025. This preliminary descriptive evidence suggests that investors are still significantly discriminating tech and AI-related firms by their idiosyncratic earnings prospects. Such discrimination tends to vanish during bubble episodes and other periods when sentiment and herd behaviour substantially shape valuations (Baker and Wurgler 2006, Chang et al. 2000). ## Evidence from an equilibrium dividend discount model A stock’s price should be equal to the present discounted value of its future dividends. Elevated P/E ratios may signal optimism about future profit and dividend growth, optimism that could prove either well-founded or misplaced, thereby exposing the market to the risk of abrupt corrections. In Albori et al. (forthcoming), we use an equilibrium dividend discount model (Molodovsky et al. 1965, Fuller and Hsia 1984, Sorensen and Williamson 1985) to compute the abnormal growth rates of earnings that would justify current equity valuations, that is, make them compatible with rational pricing in line with historical norms. Table 2 shows the model-implied growth rates, together with recent earnings growth rates and analysts’ expected growth rates, all inflation-adjusted. Table 2 Mag10 recent, expected, and model-implied growth in earnings per share (percent) ###### Source: LSEG, our estimates. Note: Real (inflation-adjusted) annual growth rates in earnings per share (EPS). For Tesla, historical EPS growth is over the past four years; for Palantir, the past year. Growth expected by I/B/E/S analysts refers to the average growth rate over a period of three to five years. Model-implied growth rates are derived from P/E ratios as recorded on 28 July 2025. For the six largest companies in our sample, the average model-implied annual growth rate is 12%, with moderate dispersion. This indicates a significant deceleration from the 33% average growth observed over the past five years and is also more conservative than the 15% growth expected by analysts. For the remaining four companies, the mean model-implied growth rate is 41%, which compares with 39% in the previous five years and 17% expected by analysts. As discussed in the previous section, these four companies are radically different from the other six, in that they are much smaller and have recently entered fast-growing AI-related markets. Their implied growth rates are compatible with scenarios – far from being guaranteed, but also not utterly unrealistic – in which they manage to scale up their new business lines at a pace similar to that kept in recent years. In summary, current equity valuations for the top US tech companies are rational if one expects high, but not historically unusual growth rates for the coming years. Apart from historical comparisons, determining if these expected growth rates are realistic is challenging. This assessment relies heavily on subjective judgments about market developments, particularly the future demand for AI-related services and improvements in productivity and profitability driven by AI adoption. In the following section, we present information that may help inform these judgments. ## Factors affecting future earnings growth The main factors that contributed to the spectacular earnings growth of US tech companies in recent years (Figures 5 and 6) were: - Strong increases in global spending on digital advertisements (by 15% annually on average between 2019 and 2024; Kemp 2025), often driven by the displacement of traditional advertisements (e.g. on TV and newspapers) and by the rise of e-commerce at the expense of brick-and-mortar stores. Amazon, Google, and Meta are market leaders in this space and have continued to expand their market shares (in 2024, they accounted for 51% of global ad sales; BestMediaInfo Bureau 2025). - Increased corporate spending on both traditional and AI-related cloud services (up by 21% annually on average between 2019 and 2024; Slingerland 2025). Amazon, Google, and Microsoft are the main providers of these services, with a combined global market share of 63% (Brindley and Zhang 2025). Significant fractions of the hardware bought by these players to scale up their data centres is provided by NVIDIA and Broadcom. - Still rapid global expansion of the number of smartphones (+5% average yearly growth between 2019 and 2024), coupled with the continued increase in the amount of time spent by users in the interaction with these devices (Howarth 2025). This has sustained the sales of phones (Apple, Google) and consumer-oriented cloud services (Apple, Google, Microsoft), and has helped fuel the previous two trends (spending on digital ads and cloud infrastructure). - Gains in efficiency driven by economies of scale and AI adoption. For example, software development represents a significant fraction of the cost of offering the products and services mentioned above, but its cost tends to increase less than proportionally as the user base expands (the same piece of software serves more users). Moreover, AI has already facilitated the automation of labour-intensive tasks, such as content-moderation on social platforms (Meta). Reportedly, AI has also improved the economics of digital advertising, by allowing for a better targeting of users. According to the latest quarterly reports of the Mag10 (which on average showed still sustained growth), as well as to the expert sources cited above, none of these trends is expected to end abruptly in the near future, although some of them are expected to slow down. For example, smartphone adoption is expected to be sustained mostly by emerging markets, as advanced ones have already been saturated (Bellan 2025). Similarly, future growth in digital-advertisement sales may be hindered by the already large market shares held by the Mag10, although also in this case emerging markets represent a significant expansion opportunity. The growth prospects of cloud services (and related hardware) are probably the most controversial ones. While some analysts expect AI adoption to keep propping up cloud spending for the foreseeable future (Goldman Sachs 2024), others deem that current AI spending by corporations is bubbly and likely to slow down, as the economics of AI applications are often unproven (Widder and Hicks 2025). Whatever the stance on this issue, most analysts seem to agree that AI spending is the key factor that will determine whether tech companies are able to achieve the high growth rates envisaged by financial analysts and embedded in stock prices (e.g. our model-implied estimates). While taking a position in this debate is complex, we note that some recent trends seem to support the view that the demand for AI services will keep increasing at a brisk pace: - As noted in a recent Economist article, the use of large language models (LLMs), such as ChatGPT, is quickly displacing Internet navigation (15% search traffic drop in the past year), as users find it more convenient to receive responses to their queries from an LLM rather than sifting through multiple websites (The Economist 2025a). This trend has just started, and it is likely to continue. The consequence is that a large portion of the revenues and profits previously made by millions of web-site owners and content creators will be shifted to cloud-service providers and hardware vendors (Godoy 2025, Weiss 2024), as on-the-fly content creation by LLMs requires enormous amounts of computational resources. At present, some of the major AI players still report difficulties in meeting these computational requirements, despite the huge investments in infrastructure made in previous years. - Job destruction by generative AI technologies (not only LLMs, but also image and video generators) is reportedly gaining traction and economic significance not only in the website-ownership space, but also in adjacent occupations (ad creatives, illustrators, writers and copywriters, photographers, models, translators, moderators, etc.) (Bartholomew 2025). For example, according to a 2024 survey conducted by the Society of Authors (SOA Policy Team (2024), 26% of illustrators and 36% of translators report having already lost work due to generative AI, and three-quarters of them expect future income reductions. As explained in the previous point, this kind of displacement creates demand for the hardware and cloud services sold by the Mag10. - AI technology is being increasingly adopted in the automotive sector (not only in autonomous vehicles, but also for driving assistance and infotainment). This trend – which will also significantly prop up the demand for cloud services, given the huge size of the automotive sector – has begun only recently, and it seems unlikely to abate, given that most auto-industry executives expect vehicles to become increasingly ‘software-defined’ (IBM 2025). These are just some examples of large markets where substantial AI adoption and, in some cases, the consequent displacement of jobs are not just a future possibility, but something that is already happening at scale. Mechanically, this adoption generates demand for compute, which is mostly offered by large US tech firms. ## Conclusions The largest US technology companies have recently recorded substantial increases in their valuations, prompting financial analysts and policymakers to express concerns about the risk of a stock market bubble. In this column, we present model-based evidence suggesting that the earnings growth rates required to justify current prices do not appear implausible given the underlying drivers of recent and expected performance. We also argue that the adoption of generative AI technologies will be a major engine of growth, and it is already transforming markets and displacing jobs at scale. Its rollout fuels rising demand for computing power, largely supplied by US tech companies, reinforcing their dominance. However, some cautions are in order. Some industries still struggle to identify reliable applications, sparking short-term disillusionment. This could temporarily curb corporate AI spending and drive a re-pricing of technology stocks (The Economist 2025b). Added to this, intensifying competition, particularly from China, threatens to erode big tech’s profit margins (Mims 2025). These factors could make the realisation of the growth rates implied by our model less certain. Authors’ note: The opinions expressed in this column are those of the authors and do not necessarily reflect the views of the Bank of Italy. ## References Albori, M, V Nispi Landi and M Taboga (forthcoming), “Is there a bubble in the US stock market? Evidence from an agnostic valuation procedure”, Bank of Italy Occasional Papers. Baker, M and J Wurgler (2006), “Investor sentiment and the cross‐section of stock returns”, The Journal of Finance 61: 1645-1680. Bartholomew, J (2025), “It’s happening fast’ – creative workers and professionals share their fears and hopes about the rise of AI”, The Guardian. Bellan, R (2025), “Apple: Pay attention to emerging markets, not falling China sales”, TechCrunch. BestMediaInfo Bureau (2025), “Google, Meta, Amazon’s combined share of global ad revenues hits 51% in 2024: Magna”. Brindley, R and Y Zhang (2025), “Worldwide cloud service spending to grow by 19% in 2025”, Canalys. Chang, E C, J W Cheng and A Khorana (2000), “An examination of herd behavior in equity markets: An international perspective”, Journal of Banking & Finance 24: 1651-1679. Duguid, K, G Steer, H Clarfelt and I Smith (2025), “Wall Street ‘euphoria’ sparks bubble warnings”, Financial Times. Fuller, R J and C C Hsia (1984), “A simplified common stock valuation model”, Financial Analysts Journal 40: 49-56. Godoy, J (2025), “Google's AI previews erode the internet, US edtech company says in lawsuit”, Reuters. Goldman Sachs (2024), “Cloud revenues poised to reach \$2 trillion by 2030 amid AI rollout”, GS Artificial Intelligence. Howarth, J (2025), “How Many People Own Smartphones?”, Exploding Topics. IBM (2025), “Automotive in the AI era”, IBM Institute for Business Value. Kemp, S (2025), “Digital 2025: Global advertising trends”, DataReportal. Marks, H (2025), “Is the US stock market in bubble territory?”, Financial Times. Mims, C (2025), “Silicon Valley’s New Strategy: Move Slow and Build Things”, The Wall Street Journal. Molodovsky, N, C May and S Chottiner (1965), “Common stock valuation: Principles, tables and application”, Financial Analysts Journal 21: 104–123. Slingerland, C (2025), “90+ Cloud Computing Statistics: A 2025 Market Snapshot”, CloudZero. SOA Policy Team (2024), “SoA survey reveals a third of translators and quarter of illustrators losing work to AI”, The Society of Authors. Sorensen, E H and D A Williamson (1985), “Some evidence on the value of dividend discount models”, Financial Analysts Journal 4: 60-69. Stacey, S and M Novik (2025), “How big is the stock market’s America bubble?”, Financial Times. The Economist (2025a), “AI is killing the web. Can anything save it?”. The Economist (2025b), “Welcome to the AI trough of disillusionment”. Weiss, T (2025), “The Decline of Stack Overflow”, Data Science with R. Widder, D G and M Hicks (2025), “Watching the Generative AI Hype Bubble Deflate”, Digital Life Initiative, Cornell University.
Shard	36 (laksa)
Root Hash	12878714870991386236
Unparsed URL	org,cepr!/voxeu/columns/unpacking-us-tech-valuations-agnostic-assessment s443