Big Tech earnings put to test as analysts cling to bullish targets amid AI bubble burst

Equity markets are pricing financing strain, over-ordering and a possible demand cliff. The latest broker research, however, still models multi-year earnings growth and, in several cases, describes forward valuations as trough or near-trough. That has made the conversion of AI capex into durable revenue the central test for the group. Meta: strong model, financing question Meta carries one of the stronger research cases among the hyperscalers.

Mizuho raised its target to $950 from $750, citing stronger confidence in Muse monetisation and 24% revenue growth in 2027. Bloomberg’s average target stands at $847, with 91% of analysts rating the stock Buy. The caveat is capital intensity. Analysts acknowledge potential equity-raise risk in early 2027 if AI infrastructure spending continues to outrun free cash flow. The longer-term thesis (advertising plus AI-driven engagement) remains intact in the notes, but the financing question has become harder to ignore.

Microsoft: the compounder case Microsoft’s research case remains centred on Azure and AI services. The stock holds 91.5% Buy ratings and a Bloomberg average target of $637, implying more than 20% upside. Valuation has compressed, yet brokers have not abandoned the multi-year compounding story. The view is that Azure growth, enterprise AI adoption and the durability of Microsoft’s software franchise justify continued investment, even if near-term free cash flow is pressured by capex.

Microsoft is therefore the cleaner hyperscaler exposure for investors who believe AI spending will convert into recurring cloud revenue rather than remaining a pure infrastructure cost. Amazon: AWS utilisation is the tell Amazon’s bull case rests on AWS growth and improving utilisation. Bloomberg shows 95% Buy ratings and an average target of $329. The notes continue to treat AWS as a multi-year compounder.

The key variable is whether AI workloads translate into sustained cloud revenue and higher utilisation, rather than simply forcing Amazon to build capacity ahead of demand. Amazon is also exposed to the custom-silicon shift: its Trainium chips form part of the broader move by hyperscalers to reduce dependence on merchant GPUs. That makes the company both a beneficiary of AI infrastructure spending and a potential source of competitive pressure on pure-play chip suppliers.

Alphabet: cloud strength, Search softness Alphabet’s recent broker notes highlight accelerating cloud demand, though Search growth has slowed. The stock still carries nearly 90% Buy ratings and a Bloomberg average target of $395. The research logic is that cloud and AI infrastructure spending will outweigh near-term Search softness. Alphabet’s TPU strategy gives it a degree of control over its AI compute costs, but it also illustrates the wider trend of hyperscalers directing more capital expenditure toward custom silicon.

Apple: the contrast at the edge Apple sits at the edge of this group. Consensus rating has slipped to 3.89, with only 58% of analysts on Buy. Product-cycle concerns and China exposure dominate the more cautious notes. Apple is no longer treated as a pure AI-infrastructure name; its valuation is being driven by hardware demand, services growth and China risk rather than data-centre capex. That makes it a useful contrast: it carries a higher forward multiple than the AI enablers without the same direct leverage to the build-out.

AI stocks slid again on Tuesday, November 3, as investors questioned whether the sector’s vast infrastructure build-out can earn a return fast enough to justify its financing needs. Korean chipmakers led the decline: Samsung Electronics and SK Hynix fell as much as about 5% in early Seoul trading, with the KOSPI down more than 2% by late morning.