Enterprise AI spending is soaring, but the financial payoff remains uncertain. In the latest episode of Breaking Analysis, theCUBE Research’s Dave Vellante and Crawford del Prete, former president of IDC and now senior adviser at PSG Equity, examine whether productivity gains can translate into meaningful returns for businesses investing heavily in AI.The conversation explores why improvements in individual tasks aren’t necessarily boosting company profits. Vellante and del Prete discuss the productivity J-curve, changing software business models and the importance of customer retention. They also examine how AI-native companies and established software vendors approach monetization as enterprise expectations continue to evolve.Infrastructure spending adds another layer to the debate. Vellante and del Prete examine financing, workload utilization, security and hybrid deployments, along with the shift toward usage-based pricing. Their discussion highlights what businesses need to prioritize to turn AI investments into sustainable revenue, measurable outcomes and stronger long-term financial returns.

Memory prices are soaring, but is this another familiar cycle or a lasting shift? In the latest episode of Breaking Analysis, theCUBE Research’s Dave Vellante examines Micron’s momentum, its high-bandwidth memory roadmap and 26 strategic customer agreements that could reshape how chipmakers and major buyers navigate supply and demand today.The conversation turns to what actually limits memory shipments as AI infrastructure expands. Drawing on Qualitate survey findings, Vellante explores packaging bottlenecks, product qualification and the trade-offs of high-bandwidth memory manufacturing. He also identifies the pricing, volume and margin signals that reveal whether Micron’s boom can hold up over time.

ServiceNow is making one of the more consequential and visible bets in enterprise AI. The company says AI Control Tower can discover and govern any agent, work across models and clouds, track cost and value, and shut down behavior that violates policy.That sales pitch plays directly to ServiceNow's greatest asset – its installed base. ITSM, CMDB, risk workflows, security operations and years of operating context give the company a distribution and data advantage most AI governance vendors do not have.Our research suggests that moat is real. But proprietary customer data shows a more nuanced picture. Control Tower is already strong as a governance workflow and system of action. It can inventory AI assets, connect risks to owners, route approvals and remediation, and preserve an audit trail.What the field has not broadly validated is the claim that Control Tower is a complete technical governance and runtime enforcement plane across the heterogeneous enterprise.

In Breaking Analysis episode 329, Matt Calkins of Appian, founder and CEO, examines the artificial intelligence control gap, referred to as AI, and alignment challenges in enterprise deployments. Calkins draws on their experience leading Appian to assess risks posed by autonomous enterprise agents and to recommend independent oversight, evidence-based regulation and robust agent governance. They emphasize the need for independent assessment, an accountable authority with enforcement powers and human approval for high-risk actions.This segment produced by theCUBE Research and hosted by Dave Vellante of theCUBE Research explores agent sprawl, stealth AI, operational controls for production agents and Qualitate customer data that inform evidence-based policy. The conversation highlights a public-interest standard for AI safety and practical measures for proving control so organizations can safely delegate authority.The discussion underscores that oversight tooling represents both a regulatory necessity and a commercial opportunity for enterprise vendors and that human approval must govern high-risk actions. Viewers gain insights on agent governance, enterprise deployment risks, independent assessment and operational controls for production agents.Subscribe for ongoing coverage of enterprise AI governance, agent oversight and AI safety research.

This episode examines CoreWeave's path to a durable ai cloud ahead of Fully Connected. Dave Vellante of SiliconANGLE Media, Inc., co-founder and co-CEO provides a Breaking Analysis preview. Vellante draws on theCUBE Research and proprietary interviews to evaluate CoreWeave's enterprise traction and market positioning.Vellante synthesizes 13 in-depth interviews and public financial disclosures to assess GPU scarcity, inference growth, model training demand, hyperscaler relationships and compliance tooling. They evaluate operating quality, pricing strategies and product expansion as factors to convert availability advantages into a durable AI cloud.Key takeaways include that GPU scarcity drives initial adoption but performance, cost and operating experience retain customers, according to Vellante. theCUBE Research finds inference growth stacking on model training and enterprises continuing to retain hyperscalers for broader application estates. Analysts identify gaps in compliance tooling, pilot-to-production conversion and the need for flexible pricing and financing as critical action items for CoreWeave ahead of Fully Connected. The analysis highlights implications for AI infrastructure, enterprise cloud strategy and vendor differentiation.

What happens when Salesforce creates more value outside its own interface? In the latest Breaking Analysis, theCUBE Research’s Dave Vellante and George Gilbert examine that possibility after Dreamforce 2026. They map Salesforce’s next move through the System of Intelligence framework, where business context helps agents turn customer goals into action.Vellante and Gilbert explain how headless access lets customers work through Claude, Slack or other clients instead of beginning inside Salesforce. The interface may shift elsewhere, but Salesforce can remain essential by supplying trusted data, business knowledge, workflows and controls through its enterprise AI harness, connecting understanding directly with execution.New Qualitate research shows most organizations remain in Agentforce pilots or proofs of concept. Yet 75% of customers evaluating headless access expect Salesforce spending to increase. Vellante and Gilbert consider why greater consumption does not automatically guarantee customer value, and how pricing must align with measurable outcomes as deployments scale.

The cloud shared responsibility model was initially not well understood by many customers. In fact, early adopters often believed that simply having data in the cloud meant that Amazon, or a SaaS vendor were responsible for safeguarding it. Amazon had to educate its customers and partners that security and compliance duties were split between the vendor and the client organization. In short, the vendor was responsible for securing the cloud resources but you, the buyer, were responsible for securing what you put inside the cloud; based on your policies, priorities and budget. We believe a similar but much more consequential dynamic is unfolding with respect to agentic AI. Specifically, Cloud computing divided responsibility by infrastructure layer. Agentic AI distributes authority across a chain of models, platforms, clouds, partners and customers. Our premise is the industry now needs a shared accountability model for the decisions, actions and outcomes that chain produces.In short - The cloud shared-responsibility model told customers who secures what. The agentic shared-accountability model must define who can do what, who can stop it, who can prove what happened and who pays when it goes wrong.Welcome to episode 326 of Breaking Analysis. Beyond Shared Responsibility - When AI Acts, Who owns the Blast Radius. In this Breaking Analysis, Principal CUBE Research Analyst Krista Case and I explain why the agentic era demands a new accountability model. We’ll draw on learnings from last week’s CrowdStrike Fal.Con event, where the post Mythos moment and the OpenAI/Hugging Face “accident” were front and center. We’ll also draw on other new datapoints, including conversations with CISOs at Fal.Con and Palo Alto Networks’ earnings print from last week, to unpack what we’ve defined as a new AI accountability model. We’ll also test this new model against our Sovereignty framework, developed by Amit Govrin and assess sovereignty in the context of business recovery. We’ll explore the sequence of events that leads up to the ultimate question of who pays when something goes wrong?

In the latest episode of Breaking Analysis, theCUBE Research’s Dave Vellante digs into CrowdStrike’s post-Mythos momentum and Fal.Con outlook. He connects record second-quarter performance, $333 million in net new annual recurring revenue and $5.84 billion in ARR with an expanding security platform and rising buyer urgency across the enterprise market today.Vellante explains how Falcon Flex improves consolidation and unit economics, while proprietary telemetry strengthens CrowdStrike’s competitive moat. Rebecca and Krista add on-site perspective as the conversation moves across cloud, identity, next-generation SIEM, exposure management, DeepSeek-R1 and agentic AI risks, emphasizing autonomy, blast-radius containment and recovery controls for modern security operations.

In the latest Breaking Analysis episode, theCUBE Research’s Dave Vellante and Amit Eyal Govrin examine why enterprise AI economics demand more than tracking tokens and model calls. Using Canva, Uber and Lindy as examples, Vellante and Govrin show how costs emerge when companies overlook value retained from AI investments.The conversation moves from hybrid architectures and LLM gateways to a five-pillar sovereignty model built around control. Vellante and Govrin recommend measuring cost per accepted governed outcome while owning routing, evaluations and policy. Their practical playbook helps enterprises manage budgets, vendor dependencies and failover without surrendering financial sovereignty to vendors.

NVIDIA is making a bigger bet than simply selling more GPUs. In the latest Breaking Analysis, theCUBE Research’s Dave Vellante looks at its push to turn AI compute into a financeable asset class and what that could mean for the infrastructure boom, capital markets and the timing of a potential bubble.Vellante also looks to CoreWeave and Nebius for clues about whether the model holds up. Demand and pricing remain strong, but the bigger test comes later, when scarcity eases and operators must prove that utilization, cash flow, residual values and financing can withstand a full infrastructure cycle.