Since August, a broad rally in technology stocks led by the semiconductor sector and AI computing infrastructure themes has rapidly calmed global market volatility. Two of Wall Street's financial giants, Morgan Stanley and JPMorgan Chase, have recently published reports stating that the primary driver pushing the S&P 500 higher is shifting from valuation expansion to earnings upgrades and AI commercialization realization.

JPMorgan Chase last week raised its end-2026 target from 7,800 to 8,000 points, also upgrading its earnings per share trajectory for this year and next. Morgan Stanley similarly raised its 2026 target to 8,000 points and its 12-month target to 8,300 points, explicitly stating the upgrade stems from earnings rather than valuations. At least seven Wall Street institutions now expect the S&P 500 to reach 8,000 points by end-2026.

This shift toward an earnings-driven S&P 500 hitting 8,000 is forming a new bullish consensus, strongly resonating with price action after the July deleveraging of AI infrastructure themes. The Philadelphia Semiconductor Index plunged nearly 29% from its June 22 high to its July 29 low, then rebounded about 20% from that low. South Korea's benchmark KOSPI index rebounded nearly 22% just two weeks after its July 30 low, re-entering a technical bull market. The July crash in South Korea involved heavy deleveraging of leveraged ETFs, with product sizes dropping from about $50 billion to $17 billion. However, the fundamental underpinnings of the AI-driven memory chip super-cycle did not collapse simultaneously, as the industry continues to discuss DRAM/HBM supply tightness and demand gaps through 2027. Consequently, this AI-led bull market increasingly resembles a positive feedback loop of 'deleveraging, market positioning reset, risk re-acceptance, and rising FOMO,' rather than a dead cat bounce after a peak in the AI earnings cycle.

Another Wall Street titan, Citadel, provides cash flow evidence showing this rally has moved from 'fundamental repair' to a 'self-reinforcing buying phase.' Its August report indicates S&P 500 Q2 EPS growth of about 33%, with one of the steepest earnings revision paths since at least 2000. Meanwhile, the index hit record highs, but the 12-month forward P/E ratio fell from about 23.1x in October to 20.1x, meaning earnings expansion, not multiple expansion, is driving the index higher.

The shift from 'selling shovels' to 'who uses shovels to dig profits': The ultimate metric for AI investment becomes ROIC and free cash flow.

JPMorgan Chase raised its 2026 S&P 500 target from 7,800 to 8,000 points, citing an exceptionally strong Q2 earnings season and mounting evidence that massive AI investments are translating into stronger corporate operating performance. The bank also raised its earnings forecasts for 2026 and 2027. However, the strategists, led by Dubravko Lakos-Bujas, note that elevated interest rates, geopolitical risks, and significant new supply in capital markets still constrain their valuation multiple assumptions. With 87% of S&P 500 companies having reported, they state the earnings picture 'remains robust and broadly distributed across industries.'

This strength led the bank to raise its 2026 EPS forecast to $365, implying 35% growth year-over-year, above the current consensus of $358. For 2027, JPMorgan Chase raised its EPS forecast to $420, implying an additional 15% growth. Part of this exceptional earnings growth comes from rising valuation of investments held by reporting companies. The strategists estimate these adjustments contributed about $18 to S&P 500 EPS in 2026. Excluding this, normalized 2026 EPS is about $347, still implying roughly 28% year-over-year growth, highlighting the robust underlying AI-driven profitability.

A key change this earnings season is the shift in focus from the scale of AI capital expenditure by hyperscale cloud companies to whether these expenditures generate an attractive return on invested capital. JPMorgan Chase believes the latest results provide encouraging evidence that commercial monetization is beginning. The strongest examples come from Google, Amazon, and Microsoft, with 'stronger cloud growth, expanding order backlogs, and improved operating cash flow visibility, successfully clearing the high bar set by investors.'

Amazon's AWS cloud revenue growth accelerated to 37%, Microsoft's Azure cloud revenue grew 43%, and Google's Google Cloud showed the strongest performance with record revenue growth of 82%. Order backlogs also expanded significantly. Google Cloud's backlog increased by $52 billion quarter-over-quarter to $514 billion. AWS's backlog reached $496 billion, up 36% sequentially and nearly 2.5 times the level a year ago. As these hyperscale cloud giants continue investing heavily in new capacity, these numbers provide considerable visibility into future revenue and profits. This is why JPMorgan Chase can raise its index earnings forecast even as AI CapEx heads toward about $900 billion in 2026 and over $1.2 trillion in 2027; it is betting on the business cycle of 'AI CapEx, cloud revenue, backlog, operating profit,' rather than just GPU shipments.

Morgan Stanley pushes this logic further, stating the next phase of alpha is spreading from AI infrastructure providers to AI adopters. Companies that can truly leverage AI to improve productivity, lower costs, expand margins, and generate cash flow may command higher valuations than those with just an 'AI concept.' Strategists led by Michael Wilson believe investors are becoming more discerning, increasingly rewarding companies that combine earnings growth with strong free cash flow and operational efficiency. This is why Morgan Stanley prefers hyperscalers over pure AI semiconductor stocks for the medium to long term. While chip stocks may return to a tactical uptrend after July's momentum liquidation, cloud giants possess both cash cow businesses and AI infrastructure, models, platforms, customer distribution channels, and future AI monetization optionality, offering a more complete risk-reward profile. Morgan Stanley continues to emphasize that the market is transitioning from an early-cycle bull market to a mid-cycle one, with leadership spreading from high beta to 'earnings quality and cash flow.' The bull market has not left technology, but it is upgrading from a 'tech solo' to earnings diffusion, where 'quality' is the true scarce asset in the next phase.

JPMorgan Chase provides the most important bullish evidence for the current AI bull market: the high AI-related revenue growth and multi-hundred-billion-dollar order backlogs at AWS, Azure, and Google Cloud are starting to prove that AI capital expenditure is not a pure cost black hole but is converting into visible revenue. This is why the bank can raise its index earnings forecast even as AI CapEx surges. The two Wall Street giants offer strategies that are not contradictory; JPMorgan Chase confirms the AI super-cycle is the index's earnings engine, while Morgan Stanley tells investors how the bull market should diffuse in the next phase.

With 87% of S&P 500 companies beating earnings expectations, the median earnings growth rate for Russell 3000 components rising to 15%, and improving earnings revisions across financial and consumer sectors, conditions are emerging for the market to spread from mega-cap AI stocks to high-quality financials, consumer staples, AI application layers, and broader earnings growth companies. The key selection criterion is no longer just growth, but whether growth can turn into cash. Companies with both EPS and free cash flow upgrades are generating significant excess returns, while those with EPS growth but deteriorating cash flow are already being penalized by the market.

Over a multi-month investment horizon, Morgan Stanley believes hyperscale cloud giants offer more attractive risk-reward profiles compared to AI semiconductor players. The strategists emphasize these companies' 'resilient core businesses, attractive relative valuations, and the market's not yet fully priced optionality from AI infrastructure investment returns and AI adoption.' This combination means hyperscale cloud companies can benefit from both sustained cloud business growth and improving returns on their massive AI investments.

As market leadership broadens, both JPMorgan Chase and Morgan Stanley are increasingly focusing on 'quality.' The expansion of the earnings recovery makes Morgan Stanley's strategists more confident that market opportunities are no longer limited to a few mega-cap stocks. With 87% of S&P 500 components beating earnings, the median Russell 3000 earnings growth rate of 15% being the strongest since 2021, and improving earnings revision breadth across sectors, the overall fundamental environment has strengthened significantly. Morgan Stanley's data shows that the breadth of S&P 500 earnings estimate revisions has recovered to 23%, and 76% of industry groups are seeing positive earnings revisions, both near cyclical highs. However, investors are becoming increasingly selective about the quality of this growth. Therefore, Morgan Stanley prefers high-quality companies with strong free cash flow, AI adopters, large financials, and consumer discretionary companies. Within the technology sector, over a longer investment horizon, hyperscale cloud giants remain preferable to semiconductor stocks.

The biggest risks remain clear: long-end US Treasury yields, oil prices, and corporate financing costs. JPMorgan Chase's reluctance to push valuation multiples above 20 times and Morgan Stanley's continued warnings about long-term yields essentially indicate that if this bull market continues to surge, the most optimistic scenario will be driven by earnings expansion, not a renewed reliance on multiple expansion.