1. Palantir reports tonight as the cleanest test of whether the market will still pay a scarcity multiple for demonstrable AI software monetisation.
Consensus expects Q2 revenue of roughly $1.8bn, implying c.81% yoy growth after 85% in Q1, while adjusted operating margins are expected to remain around the high-50s to 60% range. The key change is that Palantir is no longer being assessed merely as a high-growth government analytics vendor: US commercial revenue grew 133% yoy last quarter and has become the principal evidence that its Ontology and AIP architecture can translate generative AI into production workflows rather than pilot activity. The bull case is that Palantir represents one of the few application-software vendors where AI is accelerating revenue, contract size and margins simultaneously; the bear case is that growth and profitability are close to peak levels, while weaker international expansion and a still-extreme valuation leave little tolerance for sequential deceleration. A strong print would support the view that proprietary data models, workflow integration and implementation capability remain more valuable than the underlying foundation model, benefiting PLTR and, by association, NOW, SAP and selected vertical-software platforms. A miss would revive the broader “AI adoption does not equal durable software economics” debate across CRM, WDAY, DDOG and SNOW.
2. AMD’s earnings this week will determine whether the AI accelerator market is genuinely broadening beyond Nvidia or merely growing fast enough to support a distant number-two supplier.
AMD entered the quarter guiding revenue to roughly $11.2bn, above the prior $10.52bn consensus, with server CPU revenue expected to grow more than 70% yoy and adjusted gross margin around 56%. What changed is that the investor debate has moved beyond whether AMD can sell AI accelerators at all; the question is now whether MI-series deployments, hyperscaler commitments and EPYC share gains can build a sufficiently large software and networking ecosystem to sustain pricing and margins. Bulls will argue that customers urgently need a credible second source, that inference workloads are more heterogeneous than training and that AMD can bundle CPU and GPU architecture to win integrated data-centre deployments. Bears will argue that Nvidia still controls the developer, networking and systems stack, leaving AMD dependent on large customers seeking bargaining leverage rather than strategically committed ecosystem adoption. Positive evidence would benefit AMD, TSMC, HBM suppliers and Marvell while modestly challenging NVDA’s scarcity premium; weaker conversion would reinforce that hyperscaler capex can expand rapidly without materially weakening Nvidia’s platform dominance.
3. Datadog’s result later this week is arguably the most important SaaS read-through because observability sits at the intersection of cloud consumption, AI infrastructure and application monetisation.
Datadog raised 2026 revenue guidance in May to $4.30–4.34bn from $4.06–4.10bn, supported by stronger cloud-security demand, while Q2 expectations are roughly $1.08bn of revenue. The core investor debate is whether the hyperscalers’ accelerating AI build-out converts into durable observability consumption or whether customers offset higher infrastructure usage through optimisation and vendor consolidation. Bulls will argue that AI agents, distributed inference, model pipelines and expanding machine-to-machine traffic dramatically increase telemetry volumes and operational complexity, making monitoring, application performance and cloud security more compulsory. Bears will argue that Datadog’s consumption model remains exposed to optimisation cycles, while Microsoft, AWS, Google and ServiceNow increasingly bundle monitoring, governance and security into broader platforms. A strong print would provide one of the clearest confirmations that AI infrastructure capex creates a second-order software revenue pool, benefiting DDOG, ESTC, DT and cloud-security vendors; weakness would suggest that most near-term economics still accrue to semiconductors and infrastructure rather than the software layer managing them.
4. Cybersecurity is becoming the strategic convergence layer for big technology, raising category demand while increasing the risk that standalone vendors are bundled out.
Microsoft and Google have introduced specialised cyber models, ServiceNow is extending its AI Control Tower to discover, monitor, govern and secure agents across third-party systems, and recent OpenAI and Anthropic containment failures have moved autonomous-agent security from theoretical risk to an operational control problem. What changed is that cyber is no longer simply an adjacent AI use case: identity, permissions, behavioural monitoring, runtime enforcement and auditability are becoming prerequisites for enterprise-agent deployment. The bull case for PANW, CRWD, ZS, CYBR and OKTA is that agents create more identities, tools, API connections and privileged actions, materially expanding the attack surface and addressable market. The bear case is platform capture: Microsoft, Google and ServiceNow can embed security into operating systems, cloud infrastructure and workflow management, potentially reducing standalone pricing power. The likely second-order outcome is further consolidation around vendors controlling unique telemetry or enforcement points, with weaker point tools facing pressure even as total cyber spending rises.
5. The semiconductor correction has not broken the AI demand cycle, but it has changed the market’s time horizon from near-term scarcity towards late-cycle return risk.
The semiconductor index fell roughly 21% last week even as Microsoft and Amazon reported accelerating cloud growth and South Korean exports showed exceptional AI-related memory and systems demand. The trigger was not weaker current orders but mounting evidence that China is funding domestic memory and lithography capacity, alongside simultaneous expansion across the US, Korea and hyperscalers. Bulls will argue that leading-edge logic, HBM and advanced packaging remain difficult to replicate, while customer demand still exceeds available capacity and supports near-term estimate upgrades. Bears will argue that today’s shortages, pricing and long-term contracts are financing a globally synchronised capacity response, with eventual overbuild risk increasingly visible before earnings peak. The investment distinction therefore shifts towards architecture and ecosystem durability: NVDA, AVGO and TSMC remain relatively better placed, while MU, Samsung, SK Hynix, ASML, AMAT and LRCX carry greater sensitivity to memory pricing, Chinese substitution and utilisation in 2028–30. AMD’s result becomes particularly important because a credible second accelerator ecosystem would validate market expansion, but also accelerate the supply and pricing normalisation investors are already beginning to discount.
