Technology February 7, 2026

Market Overview

The Technology sector is polarized: massive AI infrastructure spending and fresh startup funding contrast with a sharp pullback in software and select chip stocks. Big cloud and AI players are signaling multi-year, elevated capex plans to support model training and custom silicon, while investors are punishing near-term revenue misses and guidance risks across software and semiconductors [4][5][19][12]. At the same time, venture capital continues to pour into AI-first startups, underpinning valuations even as public markets rotate [1][20].

Key Developments

1) Alphabet and hyperscale capex: Alphabet’s guidance for materially higher AI-related capital expenditures in 2026 reset expectations for AI infrastructure spending and outpaced peers, prompting market moves across the supply chain [4][5][19]. Broadcom and Nvidia rose on the prospect of increased Google spending, reflecting demand for both custom TPU assembly components and GPUs/accelerators in data centers [12].

2) Startup funding and valuations: ElevenLabs raised $500M at an $11B valuation, emblematic of continued private capital inflows into AI tooling and model companies despite public-market volatility [1][20]. This supports long-term demand for hosting, inference, and developer-focused infrastructure.

3) Software sell-off and AI fears: Software stocks experienced accelerated declines after recent AI product launches and positioning by competitors, as investors reprice risk around product monetization, customer spend cadence, and margin dilution from AI investments [3]. The market is separating companies with clear, monetizable AI advantages from those facing uncertain near-term ROI.

4) Semiconductor volatility and supply constraints: Arm’s shares plunged after licensing revenue missed estimates and a cloudy outlook, highlighting sensitivity of IP licensing models to end-market dynamics [2]. Qualcomm flagged memory shortages limiting mobile growth, underscoring component bottlenecks that constrain device makers and chipset demand [14]. AMD experienced a severe one-day sell-off after guidance concerns, illustrating how quickly data‑center momentum can reverse on near-term visibility issues [21][28].

5) Product and platform moves: Amazon’s discussions to integrate or leverage OpenAI models for Alexa and its broader push to make Alexa+ widely available signal intensifying competition in consumer and assistant AI, with implications for voice compute and edge/cloud hosting demand [16][23]. Microsoft’s leadership moves in cybersecurity reflect ongoing prioritization of enterprise AI security and services [18]. Snap’s mixed results show ad- and product-level execution still matters amid AI transitions [13].

Financial Impact

Capital spending increases by hyperscalers will drive outsized demand for GPUs, custom accelerators (TPUs), networking, and systems components, benefiting suppliers such as Nvidia, Broadcom, and select infrastructure OEMs in revenue and margin expansion scenarios [12][4][5]. However, near-term earnings volatility is likely: software companies face margin compression from R&D and uncertain revenue lift from AI features, leading to multiple contraction in the short term [3]. Semiconductor firms tied to mobile and gaming face headwinds from memory shortages and licensing cycles, which can materially alter quarterly guidance as seen with Qualcomm, Arm, and Nintendo-related memory issues [14][2][30]. Private-market froth (e.g., ElevenLabs) supports long-term TAM expansion for AI services but heightens expectations for monetization and IPO performance [1][20].

Market Outlook

Over the next 12–24 months, expect a bifurcated market: infrastructure and component suppliers aligned with hyperscaler AI buildouts should see durable demand if capex commitments materialize, while software vendors and chipset/IP firms with murky monetization and exposure to supply bottlenecks will remain volatile [4][5][12][2][14]. Key watch items for portfolio positioning: hyperscaler capex follow-through, memory supply trajectories, licensing trends at IP firms, and the pace at which AI features translate into sustainable revenue for software companies. Regulators and antitrust scrutiny around AI M&A (and acqui-hires) could also influence consolidation dynamics, affecting strategic M&A outcomes in the sector [25]. Companies with clear product-market fit in AI infrastructure and predictable monetization paths should be favored; avoid crowded, high-valuation AI applications lacking revenue clarity despite strong private funding signals [1][20][3].