ServiceNow delivered a decisive message to a market preoccupied with disruption: artificial intelligence is reinforcing its business rather than eroding it.

The company reported first-quarter subscription revenue of $3.67 billion, up 22% year over year, beating the high end of its guidance across topline growth and profitability metrics. Management also lifted its full-year outlook, projecting 2026 subscription revenue of $15.7 billion to $15.8 billion, implying sustained growth of roughly 22% to 22.5% and outpacing analyst expectations heading into the results.

The performance lands at a sensitive moment for the software sector. Over the past six months, valuations have come under pressure amid concerns that generative AI models from firms such as OpenAI and Anthropic could bypass traditional enterprise platforms by enabling companies to build workflows directly on top of large language models.

Chief executive Bill McDermott rejected that thesis, arguing that real-world enterprise adoption is revealing a different set of constraints — particularly around cost, governance, and operational reliability.

“The results speak a lot louder than the words. We’re now in another beat and raised quarter,” McDermott said, framing the quarter as evidence that demand remains intact even as the technology landscape shifts.

At the heart of ServiceNow’s positioning is its role as an orchestration layer — software that integrates AI capabilities into structured enterprise workflows rather than replacing them. That distinction is becoming increasingly important as companies move from experimentation to scaled deployment of AI tools.

McDermott pointed to accelerating uptake of the company’s AI offerings as a key driver of growth. ServiceNow had previously projected $1 billion in AI-related software revenue by 2026, but that figure has now been revised to at least $1.5 billion, with the potential for further upside.

“We’ll probably blow through that, too, because the acceptance of our AI solutions is just absolutely stunning,” he said.

Forward indicators support that narrative. Remaining performance obligations, a measure of contracted future revenue, rose 25% to $27.7 billion, while current RPO increased 22.5% to $12.64 billion. The expansion signals that large enterprises are not only maintaining spending but locking in multi-year commitments, even as macro conditions remain uneven.

The more consequential insight lies in how enterprises are evaluating AI economics. While direct access to large models offers flexibility, usage-based pricing structures can introduce significant cost volatility, particularly in high-volume operational environments.

McDermott said customers exploring model-centric architectures are encountering a mismatch between theoretical efficiency and practical cost. He cited a case where a chief information officer at a major client assessed using a direct AI model approach to run IT operations. According to McDermott, the model-driven setup would have cost roughly ten times more than deploying ServiceNow’s integrated AI tools.

The issue is not just pricing, but predictability. Enterprise IT budgets are typically structured around fixed or subscription-based costs, whereas AI model usage often scales with demand, making expenses harder to forecast. That unpredictability can become a constraint at scale, particularly in regulated industries where cost control and auditability are critical.

By embedding AI within its platform, ServiceNow is effectively converting variable AI costs into more predictable software spend, while also layering governance, compliance, and workflow management on top. This approach positions the company less as a competitor to model providers and more as an intermediary that translates raw AI capability into enterprise-ready applications.

McDermott was explicit in his critique of standalone AI offerings for enterprise use, describing them as “parlor tricks,” a characterization that underscores the gap between demonstration-level capability and production-grade deployment.

The broader implication is that the competitive landscape is shifting. Rather than a binary contest between traditional software and AI-native systems, the market is evolving into a layered architecture. At the base are model providers, competing on performance and scale. Above them sit platforms like ServiceNow, which integrate those models into business processes, enforce governance, and deliver measurable outcomes.

ServiceNow’s results suggest that this middle layer remains critical. Enterprises are not abandoning platforms; they are demanding that those platforms incorporate AI in ways that align with operational realities.

The company’s momentum also underpins a structural advantage: deep integration into mission-critical workflows such as IT service management, customer operations, and employee systems. These embedded positions make displacement more difficult, even as new technologies emerge.

However, the pace of AI model improvement could compress the value of intermediary layers if models become easier to deploy and manage directly. Pricing dynamics could also shift if model providers move toward more predictable enterprise licensing structures.

For now, however, ServiceNow appears to be benefiting from the transition phase. Its raised guidance, expanding AI revenue expectations, and strong forward bookings indicate that customers are prioritizing integration, reliability, and cost control over experimental flexibility.

In that context, AI is not dismantling the enterprise software stack. It is reshaping it — and, for companies able to absorb and operationalize the technology effectively, extending its growth cycle rather than ending it.

Tesla delivered an unexpected boost to investor sentiment in the first quarter, reporting a cash surplus that provides near-term breathing room as the company embarks on one of the most capital-intensive pivots in its history.

The electric vehicle maker posted free cash flow of $1.44 billion, according to LSEG data, sharply outperforming expectations for a $1.43 billion cash burn. The upside was driven in part by capital expenditures that came in roughly 40% below analyst forecasts, raising questions about the timing and pace of Tesla’s planned spending even as it prepares to deploy more than $20 billion this year on artificial intelligence, autonomy, and robotics.

Revenue for the quarter stood at $22.39 billion, slightly below expectations of $22.6 billion, reflecting ongoing pressure in Tesla’s core automotive business. While vehicle deliveries rose 6.3% from a year earlier, they fell short of Wall Street forecasts, underscoring a more complex demand environment as competition intensifies and pricing power erodes.

Chief executive Elon Musk has increasingly tied Tesla’s long-term valuation, now hovering around $1.2 trillion, to its ambitions beyond traditional car manufacturing. The company is investing heavily in self-driving technology, robotaxi networks, and humanoid robotics, betting that these areas will redefine its business model and unlock new revenue streams.

The first-quarter cash surplus offers Musk a window to advance that narrative. Investors have grown more focused on whether Tesla can translate years of promises around autonomy into commercially viable products. The company has begun expanding its robotaxi service, with deployments in Dallas and Houston following an earlier launch in Austin, though timelines have often been revised.

Tesla has said it aims to extend robotaxi operations to around seven metropolitan areas in the first half of the year. That ambition remains under scrutiny, given a track record of missed deadlines and the regulatory complexity surrounding autonomous vehicles. Approval processes are still evolving, particularly outside the United States. The Dutch regulator RDW has recently moved to seek European Union-wide clearance for Tesla’s Full Self-Driving system, a step that could open a larger market if successful but is unlikely to deliver immediate results.

But Tesla is at the same time preparing to begin volume production of its Cybercab, a fully autonomous vehicle designed without a steering wheel or pedals. The model represents a significant departure from conventional automotive design and is central to Musk’s vision of a driverless transport network. However, the commercial viability of such vehicles will depend heavily on regulatory approval, safety validation, and consumer acceptance—factors that remain uncertain.

Meanwhile, Tesla’s core automotive segment continues to face mounting headwinds. Rival automakers are introducing newer electric models, often at lower price points, eroding Tesla’s first-mover advantage. The expiration of U.S. electric vehicle tax incentives has added further pressure, reducing affordability for buyers and weighing on demand.

Tesla has attempted to respond by introducing lower-priced “Standard” versions of its Model 3 and Model Y after scrapping plans for a dedicated low-cost platform in 2024. Even so, analysts have revised down their delivery forecasts, with Visible Alpha data pointing to just 2.4% growth in 2026, to about 1.67 million vehicles. Some projections suggest deliveries could decline this year, highlighting the limits of incremental pricing adjustments in a more competitive market.

Against that backdrop, Tesla’s energy generation and storage division has emerged as a stabilizing force. Demand for grid-scale battery systems has remained strong, driven by the expansion of renewable energy and the need for grid stability. This segment is increasingly viewed by investors as a critical secondary growth engine, offering more predictable revenue compared to the cyclical auto business.

The divergence within Tesla’s operations is becoming more pronounced as the company is grappling with slowing momentum in its core vehicle segment. It is also committing significant capital to technologies that remain largely unproven at scale but carry the promise of higher margins and long-term growth.

The lower-than-expected capital expenditure in the first quarter may provide short-term relief for cash flow, but it also raises questions about execution. If spending accelerates later in the year, as management has indicated, it could quickly absorb the current surplus, tightening financial flexibility.

For now, the market appears willing to give Tesla the benefit of the doubt, buoyed by the stronger cash position and continued belief in Musk’s long-term vision. But that confidence is increasingly contingent on tangible progress. As the company moves deeper into its AI and robotics strategy, the margin for delay is narrowing.

The first quarter, in that sense, offers a temporary reprieve rather than a resolution. Tesla has bought itself time. The question is whether it can use that time to convert ambition into measurable results.