Every major strategist covering equities is running the same mental comparison \u2014 lining up today\u2019s AI boom against the late 1990s internet buildout and asking where we are in the cycle. Evercore ISI\u2019s Julian Emanuel says<\/a> the post-March 2026 rally \u201cfeels like 1999 \u2026 relatives, friends, doctors, Uber drivers are all talking about AI\/Tech stocks.\u201d On the other side, veteran chip analyst-turned-fund manager Dan Niles argues the closer parallel is 1997<\/a> \u2014 years three and four of an infrastructure buildout with real runway remaining. Both men are staring at the same market and seeing different years.<\/p>\n The Goldman Sachs equity strategy team led by Ben Snider put a fine point on the structural problem this week. In their\u00a0Weekly Kickstart, they describe the S&P 500 as \u201cone big trade\u201d: technology accounts for 85% of the index\u2019s 10% year-to-date return, and Nvidia<\/a> alone \u2014 at 9% of S&P market cap \u2014 has contributed 20% of the aggregate YTD return.<\/p>\n The Goldman Momentum factor, which now moves in near-lockstep with AI stocks, has surged 25% in three months, one of its sharpest rallies on record. Goldman notes that in past episodes of comparably sharp momentum rallies with the market near a high \u2014 including mid-1998, late 1999, and late 2021 \u2014 subsequent S&P returns were soft. The market breadth signal is flashing a similar warning: the median S&P 500 stock sits 13% below its 52-week high even as the index registers new records.<\/p>\n The case for 1999<\/p>\n Apollo\u2019s Torsten Slok has been sounding the concentration alarm with data that is hard to dismiss. The S&P is no longer a diversified index, he argues on his Daily Spark blog<\/a> \u2014 it\u2019s a vehicle dominated by a small number of extraordinarily profitable tech companies, and capital is piling in across every asset class. <\/p>\n Goldman\u2019s economics team, in a separate note by Pierfrancesco Mei published Saturday, deepened the structural case: rising concentration has accounted for roughly\u00a0one-third\u00a0of the increase in U.S. corporate profit margins since 2000, and the industries most exposed to AI \u2014 information, finance, professional services \u2014 are already the most concentrated and highest-margin sectors in the economy. When AI accounts for nearly all VC funding and half of IG bond issuance, that\u2019s not diversification of the AI thesis. It\u2019s doubling down on it across the entire capital markets stack.<\/p>\n The case for 1997<\/p>\n LPL Financial\u2019s Jeff Buchbinder offers the counter. The Nasdaq-100 is up roughly 140% since ChatGPT launched \u2014 impressive, but nowhere near the 1,090% gain the index posted between Netscape\u2019s debut and the dotcom peak in March 2000. Tech valuations sit around 25x forward earnings today; at the 2000 peak they were 58x. <\/p>\n Goldman\u2019s Mei agreed that the valuation picture looks different from the dotcom era: breadth today is \u201cfar less narrow than the market in 1999-2000,\u201d and current market concentration \u2014 while extreme \u2014 has not reached dotcom-era levels. Critically, the AI\u00a0adoption\u00a0phase has barely begun; the current buildout is still centered on infrastructure.<\/p>\n Morgan Stanley\u2019s broadening thesis: A third view<\/p>\n Morgan Stanley\u2019s Michael Wilson offered a different frame entirely that complicates the 1997\/1999 binary. In his mid-year outlook, published this month, the bank\u2019s chief U.S. equity strategist raised his S&P 500 year-end 2026 target to 8,000 and his 12-month target to 8,300, insisting \u201cthis is an earnings story, not a multiple expansion one.\u201d His core argument: the AI rally is masking a genuine broadening of earnings growth beneath the surface. S&P 500 EPS revision breadth just hit a four-year high at 24%, median stock earnings growth is running at 16% \u2014 double the trailing four-quarter average \u2014 and small-cap forward earnings growth is approaching 20%.<\/p>\n Gad Levanon, chief economist of the Burning Glass Institute, similarly waved aside the bubble talk in a recent conversation with Fortune. <\/p>\n \u201cAnytime there\u2019s a huge increase in stock prices for a long time, people will think that there is a bubble,\u201d he said. \u201cBut at the same time, we also have a huge increase in the earnings of those companies, so it\u2019s not based on nothing.\u201d<\/p>\n Maybe there is some overvaluation and you can never rule out bubbles in markets, Levanon allowed, but if you look at the revenue growth of the Magnificent Seven, \u201ccertainly a lot is happening.\u201d He said it\u2019s hard to use some new tools coming online, Claude Code from Anthropic in particular, \u201cand not think that it\u2019s transformative.\u201d Levanon said it\u2019s obvious to him that there will be \u201chuge usage\u201d of these tools in many professions.<\/p>\n Goldman\u2019s read of the data supports these arguments in part: EPS revisions have been positive in every S&P sector over the past month, and consensus 2026 and 2027 EPS estimates have each risen 8% YTD \u2014 driven primarily by AI infrastructure and energy capex. Yet when you strip out AI infrastructure and energy companies, 2027 EPS estimates are essentially flat YTD. The broadening, in other words, is real but fragile \u2014 and it lives almost entirely inside the AI story.<\/p>\n What Goldman\u2019s margins research adds<\/p>\n Goldman economist Mei\u2019s work on concentration and margins provides the deepest structural context of any of these notes. The core finding: new technologies have historically\u00a0raised\u00a0concentration rather than democratized it, because scale advantages accrue to the firms with the capital and organizational capacity to deploy technology first.<\/p>\n The sales share of the top 1% of U.S. firms rose from roughly 60% in the 1960s and \u201970s to around 80% in recent years \u2014 and concentration has risen fastest during periods of rapid technological change. The implication for AI is explicitly two-sided: AI could plausibly foster greater competition in some high-margin, high-concentration sectors; but history suggests the more likely outcome is that the most successful AI adopters pull further ahead, intensifying concentration and sustaining elevated margins for the leaders. <\/p>\n That dynamic is already visible. Morgan Stanley\u2019s data show that excluding semis, hardware, and power stocks, long\/short hedge fund exposure is at a 10-year low \u2014 meaning most of the market is being left behind as a narrow group captures the AI gains.<\/p>\n Why it matters<\/p>\n The 1997-or-1999 question isn\u2019t academic. If Buchbinder and Wilson are right, investors rotating out of AI-adjacent tech now are leaving the best part of the cycle\u2019s returns on the table. If Slok\u2019s concentration data \u2014 and Goldman\u2019s momentum analysis \u2014 are the more telling signal, the market\u2019s apparent health is a top-10-company illusion.<\/p>\n The honest answer may be that the 1997\/1999 framing is itself a symptom of bubble brain \u2014 the irresistible human urge to map the present onto the past. This time is different in ways that matter (earnings-backed, cash-flow-funded, infrastructure-phase growth rather than adoption-phase growth) and similar in ways that should make investors nervous (record concentration, record momentum, and a market that is, as Goldman puts it, \u201cone big trade\u201d). The question is whether \u201cdifferent enough\u201d is a sufficient margin of safety.<\/p>\n For this story,\u00a0Fortune\u00a0journalists used generative AI as a research tool. An editor verified the accuracy of the information before publishing.<\/p>\n","protected":false},"excerpt":{"rendered":"The numbers are hard to ignore. The top 10 companies in the S&P 500 now account for 34%…\n","protected":false},"author":2,"featured_media":42888,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[24,25,302,3775,286],"class_list":["post-42887","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai","tag-ai","tag-artificial-intelligence","tag-bubble","tag-earnings","tag-markets"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/42887","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=42887"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/42887\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/42888"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=42887"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=42887"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=42887"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"](\"https:\/\/www.europesays.com\/ai\/wp-content\/uploads\/2026\/05\/nvidia.png\"\/)
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