Global AI Investment Fractures: Why Investors Now Favor Hardware Over Software

ByInnoVirtuoso

If the AI boom was once a single, roaring river, it’s now splitting into fast-moving channels—some racing ahead, others hitting rocks. The surprise? The biggest winners today aren’t the flashy AI apps or office copilots. They’re the “picks and shovels” of the new digital gold rush: the chips, memory, networks, and power infrastructure behind sprawling AI data centers. The result is a market that’s getting smarter—and harsher—about who actually turns AI hype into profit.

As reported by the Journal Record on Feb 9, 2026, cracks are widening across stocks, sectors, and regions as investors separate near-term cash flow from long-term speculation. Software names with fuzzy AI return profiles are under pressure, while the companies enabling AI at the physical layer are holding up—and in some cases, soaring. This is not a rejection of AI. It’s the market demanding tangible value.

In this deep dive, we’ll unpack the fracture lines: why hardware is posting relative strength, how capex is being re-priced, what this means for software business models, and where the next wave of durable returns could emerge.

Source: Global AI Trade Fractures as Investors Favor Hardware Over Software (Journal Record)

Note: This article is for informational purposes only and is not investment advice.

The AI Trade Is Maturing—And the Market Is Getting Disciplined

For nearly two years after ChatGPT’s breakout in 2022, markets moved in unison: anything AI-adjacent rallied. That phase is over. According to the Journal Record, software standouts in enterprise AI—like ServiceNow and Salesforce—saw shares drop 12% and 9% respectively in the latest week, while Europe’s data analytics giant RELX tumbled 16.4%. By contrast, semiconductor names dipped less. Memory leaders in South Korea, including Samsung Electronics and SK Hynix, have surged 32% and 29% year-to-date on an AI-fueled “memory mania.”

Charu Chanana of Saxo Bank frames this not as a repudiation of AI, but as investor discernment finally taking the wheel. That translates to: prove the ROI, or prepare for turbulence.

The “Picks and Shovels” Pivot: From Apps to Infrastructure

Investors are revisiting an old strategy with new urgency: back the infrastructure that everyone needs, regardless of who wins in application-layer software. In AI, that means GPUs, accelerators, memory (especially high-bandwidth memory), networking, power systems, cooling, and the data center build-out itself.

  • What’s a “picks and shovels” play? Think Levi Strauss selling jeans to gold miners—or, in tech terms, the shippers, routers, and silicon behind every AI product. For a primer, see: Picks and Shovels Play (Investopedia).
  • Why it’s working now: Demand for AI compute has outpaced supply; capacity remains constrained by chips, advanced packaging, and power availability. When a scarce input is crucial and non-optional, the value accrues to the enablers—at least in the buildup phase.

Semiconductors, Networking, and Power: Where the Margins Flow

While not immune to pullbacks, semiconductors have shown relative resilience versus software peers with murkier AI monetization. Beneath the hood:

  • Compute accelerators: AI training and inference lean heavily on advanced GPUs and custom accelerators.
  • Networking and interconnects: High-speed fabrics are essential to move data across clusters at scale.
  • Memory: High-bandwidth memory (HBM) is critical for model performance. Constrained supply and rising content per accelerator have bolstered pricing power.

The standouts YTD—Samsung Electronics (+32%) and SK Hynix (+29%), per the Journal Record—underscore the thesis. AI demand is structurally lifting memory intensity per server. That creates a revenue tailwind that can persist until capacity meaningfully catches up.

Caveat: this is still a cyclical industry. Memory is notorious for booms and busts. Today’s “mania” could cool if supply ramps faster than expected or if AI demand normalizes.

The Underappreciated Bottleneck: Power and Real Estate

Even if chips are available, data centers face constraints in power provisioning, transmission, and cooling. Utilities, grid operators, and data center landlords are now part of the AI value chain. For investors and operators alike, power access is becoming a moat.

Software Under Pressure: The Monetization Gap

Why are software stocks wobbling while AI enthusiasm remains high? Because investors are asking harder questions:

  • Does AI expand your revenue per customer—or cannibalize existing seats?
  • Will inference costs (compute and memory) eat into your gross margins?
  • Can you defend your offering as foundation models become commodities?
  • How fast will pilots convert to production with measurable payback?

Recent share moves reflect these doubts. The Journal Record notes ServiceNow (-12%) and Salesforce (-9%) under pressure, and European analytics leader RELX (-16.4%) taking a hit. When AI features are bundled as “table stakes,” monetization can lag adoption. If customers aren’t yet paying a premium—or if usage costs scale faster than revenue—margins compress.

The Software Models That Can Still Win

This is not a blanket indictment of software. It’s a filter. The software players that break out in 2026–2027 will do at least one of the following:

  • Deliver measurable, short-cycle ROI: Think revenue-generating workflows (sales productivity with verified uplift), cost-out automations with clear time savings, or risk reduction with quantifiable outcomes.
  • Own critical infrastructure with switching costs: Vector databases, orchestration layers, guardrails, evaluation/observability, and MLOps can earn durable seats if embedded deeply into enterprise stacks.
  • Exploit proprietary data or distribution: Vertical AI vendors built around unique datasets (and outcomes that general models can’t easily replicate) can carve defensible niches.
  • Price smartly: Usage-based models that align with value, plus optimization to keep inference COGS in check, will separate the winners.

Five Practical KPIs to Evaluate AI Software

  • AI revenue as a percentage of ARR—and its growth trajectory
  • Gross margin trends post-AI feature launches
  • Customer case studies with quantified payback periods (in months, not years)
  • Expansion metrics: AI attach rate to existing seats, ARPU lift
  • RPO/backlog growth tied explicitly to AI modules or SKUs

Capex Is No Longer a Flex: ROI or Bust

In 2023–2024, hyperscalers could announce bigger capex and be cheered. In 2026, investors want the receipts. The Journal Record highlights diverging reactions among the Magnificent Seven:

  • Microsoft shares fell 10.4% despite higher spending
  • Meta rose 10%
  • Alphabet and Amazon saw declines following capex jumps

The message is unmistakable: big spend must translate into operating leverage, product differentiation, or durable new revenue—soon. As Mark Hawtin of Liontrust warns, the group may underperform without clear ROI. Translation: the age of “capex first, justify later” is over.

A Simple ROI Framework for AI Capex

Companies can win back investor confidence with transparent math:

  • Capacity vs. demand: Link GPU hours and memory capacity to live, contracted workloads.
  • Unit economics: Cost per 1,000 tokens or per query vs. monetized price points.
  • Utilization and ramp: Show curves for utilization improvement and time-to-breakeven per data center region.
  • Margin translation: Tie AI module adoption to gross margin progression and free cash flow timing.

The more these levers are quantified on earnings calls and in investor decks, the less speculative the trade becomes.

The Magnificent Seven Divergence: Different Playbooks, Different Outcomes

The mega-cap platforms are no longer moving in a pack. Each has a distinct AI strategy, cost curve, and monetization pathway:

  • Product depth and distribution advantage matter: Copilots, ads, commerce, cloud workloads—each monetizes AI differently.
  • Not all capex is equal: Some dollars build moats (e.g., exclusive model performance, ecosystem lock-in), others subsidize commoditized compute.
  • Investor communication is a differentiator: Companies that articulate clear milestones on usage, pricing, and margins tend to be rewarded.

For reference: – Microsoft IR: https://www.microsoft.com/investor – Meta IR: https://investor.fb.com – Alphabet IR: https://abc.xyz/investor – Amazon IR: https://ir.aboutamazon.com

Regional Realignment: US, Europe, and Asia Split Paths

The maturing AI trade isn’t just about sectors; it’s also about regions:

  • United States: Mixed outcomes as investors parse capex ROI and software monetization.
  • Europe: Analytics and data-centric software names like RELX have come under pressure, reflecting caution around AI’s impact on legacy workflows.
  • Asia: South Korea’s memory champions—Samsung Electronics and SK Hynix—are rallying on AI-driven demand for advanced memory and packaging.

Policy, export controls, and local supply chains all factor into regional performance. Memory, foundry, and advanced packaging capacity in Asia remains central to the AI stack, while North America dominates in cloud platform scale and enterprise distribution.

Helpful links: – Samsung Electronics IR: https://www.samsung.com/global/ir/ – SK Hynix IR: https://www.skhynix.com/ir/ – RELX: https://www.relx.com – ServiceNow: https://www.servicenow.com – Salesforce IR: https://investor.salesforce.com – Saxo Bank: https://www.home.saxo/ – Liontrust: https://www.liontrust.co.uk

What This Market Wants to See Next

Investors aren’t asking for miracles—they want clarity, discipline, and proof points. The signals that can shift sentiment:

  • HBM supply/demand updates and pricing trends
  • GPU/accelerator lead times and delivery schedules
  • Power availability and data center onlining cadence in key regions
  • Quantified enterprise ROI from AI pilots going production
  • Software gross margin stabilization post-AI launch
  • Capex guidance connected to free cash flow inflection
  • AI attach rates in large enterprise deals
  • Regulatory clarity on data use, privacy, and AI safety
  • Model differentiation that matters in production (not just benchmarks)
  • Consolidation or partnerships that reduce go-to-market friction

The Builder’s Playbook: Founders and Operators

For teams building in the AI economy, the bar just went up. But the path to winning is clearer:

  • Design for ROI from day one: Pick use cases where time savings, revenue lift, or risk reduction can be measured in weeks.
  • Control your COGS: Optimize prompts, caching, model choice, compression, and hybrid inference to keep gross margins healthy.
  • Own the last mile: Industry-specific workflows, integration depth, and data feedback loops beat generic chat interfaces.
  • Be multi-model, pragmatic: Use the right model for the job; negotiate with hyperscalers; exploit credits wisely.
  • Prove it with customers: Referenceable case studies with quantified outcomes will do more for valuation than a bigger model ever could.

The Investor’s Playbook: A Barbell With Discipline

As the AI stack stratifies, many investors are adopting a barbell approach:

  • On one side: Enablers with clear near-term demand visibility (chips, memory, networking, power, data center ecosystem).
  • On the other: Software with demonstrable ROI and moat—either via proprietary data, workflow lock-in, or critical infra with high switching costs.

In the middle: generalized apps without edge, or software adding AI features without changing customer economics meaningfully. In a maturing market, that’s where multiples compress.

Risk Map for 2026

The road ahead is not linear. Key risks to watch:

  • Macro and rates: Higher discount rates punish long-dated AI payoffs.
  • Supply normalization: If chip and memory supply grow faster than demand, enabler margins could compress.
  • Power bottlenecks: Capacity delays can push revenue recognition out and inflate costs.
  • Model commoditization: If quality converges, software differentiation must come from data, UX, and workflow—not the base model.
  • Regulatory shifts: Privacy, data localization, and AI safety rules could reshape costs and timelines.
  • Trade and geopolitics: Export controls or supply chain frictions can reroute profit pools and delay deployments.
  • Debt loads: Elevated capex funded by leverage raises balance-sheet risk if ROI lags.

2026 Outlook: From Hype to Hard Numbers

So where does this go? Expect the gap between storytellers and scorekeepers to widen. The market is rewarding companies that can:

  • Translate capex into paying workloads quickly
  • Show improving unit economics in AI products
  • Monetize distribution advantages without sacrificing margins
  • Secure scarce resources (chips, memory, power) at favorable terms
  • Publish credible, repeatable ROI case studies

In other words, AI’s center of gravity is shifting from possibility to productivity. The winners won’t just talk about models—they’ll operationalize them.

FAQs

Q: Why are hardware and memory names outperforming AI software now?
A: Because they sell essential inputs into every AI workload. With demand outstripping supply for compute, memory, and power, enablers have pricing power and clearer near-term revenue visibility. Software monetization is real but lags if customers don’t pay more or if inference costs squeeze margins.

Q: Does this mean the AI bubble is popping?
A: Not necessarily. As Saxo Bank’s Charu Chanana suggests, the market is getting discerning, not dismissive. Money is rotating toward companies that can show tangible value and returns. It’s a maturation, not a collapse.

Q: What is a “picks and shovels” strategy in AI?
A: It’s backing the infrastructure everyone needs—chips, memory, networking, data centers, power—regardless of which software apps win. Learn more via Investopedia.

Q: Can software companies still win big in AI?
A: Absolutely—if they prove ROI and defend margins. The best-positioned software will deliver measurable business outcomes, own critical infrastructure layers with high switching costs, and leverage proprietary data or distribution to stay differentiated.

Q: Why are investors scrutinizing capex so closely now?
A: Because the bill is huge, and money isn’t free. Investors want to see capex translate into utilization, revenue, and cash flow on a predictable timeline. The Journal Record notes mixed reactions among the Magnificent Seven: some were rewarded, others penalized, based on perceived ROI discipline.

Q: Are memory chip gains sustainable?
A: AI workloads are structurally memory-hungry, which is bullish for demand. But memory remains cyclical. If supply ramps aggressively or AI demand growth slows, pricing power can fade. Watch HBM capacity additions and ASP trends.

Q: What KPIs should I track to gauge AI monetization?
A: For software: AI-driven ARR, gross margin trends, attach rates, ARPU lift, and referenceable ROI. For enablers: backlog, utilization, capacity onlining schedules, and pricing/mix. For hyperscalers: capex-to-FCF conversion and workload utilization.

Q: Should I avoid AI software stocks altogether?
A: Not necessarily. Focus on those with clear ROI, durable moats, and disciplined pricing. Avoid generalized “AI features” that don’t change customer economics or that inflate COGS without commensurate pricing power.

The Takeaway

AI isn’t fading—it’s focusing. Markets are rewarding the companies that power AI’s physical reality and the software vendors that can prove value in dollars and days, not demos and decks. Hardware and memory are benefiting from scarcity and necessity, while software is being asked to earn its keep. Capex is no longer a flex; it’s a promise that must be kept with utilization, margins, and cash flow.

For operators, the mandate is clarity: build where ROI is obvious, control your cost lines, and turn pilots into production fast. For investors, the edge lies in separating enablers with visibility from applications with verifiable payback. The AI story is entering its second chapter—and the plot now turns on who can turn spending into sustainable returns.

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InnoVirtuoso

Hello there! I'm passionate content writer navigating the digital landscape. With a deep love for words and an insatiable curiosity about technology.

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