Assessing the 102% and 170% Upside Cases for Datadog and Atlassian

The investment backdrop for Datadog and Atlassian is defined by a sharp, sector-wide correction. The S&P North American Technology Software Index, a benchmark for the group, has fallen 24% from the record high it reached in September 2025, confirming the sell-off has moved into bear market territory. This isn't a story of isolated company weakness; it's a fundamental repricing driven by a shift in market sentiment. For years, software stocks commanded a premium for their predictable, subscription-led growth. That confidence has now cracked as investors grapple with a central question: does artificial intelligence strengthen incumbent software vendors or threaten to hollow them out?

The fear is that AI-native tools could compress pricing, disrupt established workflows, and reset long-term growth assumptions built on steady renewal cycles. This anxiety has been clearly evident in the charts, with the iShares Expanded Tech-Software Sector ETF marking one of its steepest drawdowns since the global financial crisis. The sell-off has been swift and indiscriminate, dragging down even companies with strong fundamentals. ServiceNow, for instance, saw its stock slip alongside the sector despite posting "good, but not good enough" quarterly results that failed to change the AI narrative.

Within this broader repricing, the specific opportunity for Datadog and Atlassian emerges. Their targeted upside cases are built on the premise that the market is overreacting to disruption fears, creating a mispricing gap. Atlassian's recent slide is a stark example of the disconnect. The stock has fallen 29.3% in less than a month, from a high of $160.87 on January 7th to around $113.70. Yet, the company continues to demonstrate solid underlying health, with revenue growth of 19.5% last quarter and strong cash flow margins. The thesis is that the software bear market is a rational correction driven by legitimate AI disruption fears, but the fundamental strength and strategic positioning of these two leaders suggest their targeted upside is grounded in a potential mispricing correction.

Business Model Analysis: Resilience and AI Integration

The core investment thesis hinges on a critical tension: the market's fear of AI disruption versus the demonstrated resilience and strategic positioning of these incumbents. For Atlassian and Datadog, the narrative isn't about resisting change, but about how their entrenched business models and deliberate AI integration could make them the very conduits for it.

Atlassian's financials show robust underlying demand, a key buffer against sentiment swings. The company's third-quarter revenue grew 21% to $1.4 billion, with its remaining performance obligation (RPO) jumping 42% to $3.3 billion. This RPO surge is particularly telling; it signals a deep pipeline of committed future revenue, indicating that enterprise customers are not retreating from the platform. The company's strength lies in its multi-departmental moat. As a leader in work management for both DevOps and marketing teams, Atlassian can cross-sell and upsell within organizations, a powerful growth engine. Its AI strategy, embodied in the generative assistant Rovo, is designed to deepen that engagement. By helping DevOps teams generate and review code, and non-technical teams automate workflows, Rovo aims to make the platform indispensable. The market's fear is that AI tools will commoditize software, but Atlassian's model suggests the opposite: AI features are being woven into a dominant workflow, potentially increasing customer stickiness and lifetime value.

Datadog presents a similar story of consolidation and AI leadership. The company is recognized as a leader in AI for IT operations, a field where machine learning is critical for managing complex, distributed systems. Its platform, which consolidates a broad suite of monitoring tools, has made it the top share gainer in its core observability market. This consolidation trend is a structural tailwind; IT departments are actively seeking single-pane-of-glass solutions, and Datadog is capturing that demand. Financially, the company's third-quarter results were strong, with revenue up 28% to $886 million and RPO soaring 53% to $2.8 billion. The AI engine Watchdog automates critical tasks like anomaly detection and root cause analysis, directly addressing the operational pain points that drive observability spending. The risk here is margin pressure, as heavy R&D investment supports this innovation. Yet, the financial resilience is clear: even with less impressive bottom-line growth, the top-line momentum and massive RPO expansion point to a durable growth trajectory.

The bottom line is that both companies are navigating the AI disruption narrative not by ignoring it, but by embedding AI into their core value propositions. Atlassian is making its workflow platform smarter and more essential, while Datadog is becoming the AI-powered nervous system for IT operations. Their financial resilience-evidenced by strong revenue growth and exploding RPO-suggests the market's fear of a demand collapse may be misplaced. Instead, the data implies that for these specific leaders, AI integration is a catalyst for deeper customer relationships and continued market share gains.

Valuation and Specific Upside Case

The investment case for Datadog and Atlassian crystallizes around a stark valuation disconnect. Both stocks trade at deep discounts to their recent peaks, while Wall Street analysts see a path back to those highs driven by AI integration. Atlassian's stock is down 40% from its 52-week average price and sits 75% below its 52-week high. Datadog, while less extreme, is also well off its highs, setting the stage for a potential repricing if the AI narrative shifts.

The specific upside targets are ambitious. Analyst Keith Weiss at Morgan Stanley has set a $320 price target on Atlassian, implying a staggering 170% gain from recent levels. For Datadog, analyst Adam Shepherd at Arete Research raised his target to $260 per share, which represents a 102% upside. These are triple-digit forecasts, signaling a belief that the current market pessimism is overdone.

The financial drivers underpinning these gains are clear. For Atlassian, the catalyst is engagement. The company's work management platform is seeing more customers engage with its AI features, a sign that the generative assistant Rovo is driving stickiness. This is supported by robust underlying health: last quarter's revenue grew 19.5% and the remaining performance obligation (RPO) surged 42% to $3.3 billion. The thesis is that AI integration will boost renewal rates and average revenue per user, turning a sector-wide fear into a company-specific tailwind.

Datadog's case rests on its AI leadership and consolidation power. The company is recognized as a leader in AI for IT operations and is the top share gainer in its core observability market. Its third-quarter results showed strong top-line momentum with revenue up 28% to $886 million and RPO jumping 53% to $2.8 billion. The AI engine Watchdog is central to this growth, automating critical tasks for IT teams. The financial resilience is evident, but the valuation is steep, trading at 66 times earnings. The implied upside assumes that this premium is justified by the continued rollout of AI agents and the company's dominant platform position.

The bottom line is that these upside cases are not about ignoring disruption, but about betting on the winners. The valuation discounts provide a margin of safety, while the specific price targets are built on the expectation that AI will drive higher engagement and renewal rates for these entrenched platforms. The catalyst is the sector's ability to demonstrate that AI integration strengthens, rather than threatens, the subscription model.

Counterarguments and Risk Assessment

The targeted upside cases for Datadog and Atlassian are built on a bet that the market is overreacting to AI disruption. The competing narrative, however, is that this fear is not overdone, but a rational repricing of a structural threat. The primary risk is that AI-native tools accelerate faster than incumbents can adapt, leading to prolonged pricing pressure and margin compression across the sector.

For Atlassian, the specific vulnerability is that its work management platform could be repositioned as a commoditized layer. The fear is that AI-native tools, designed from the ground up for code generation or workflow automation, could bypass traditional workflow interfaces. This would threaten the company's multi-departmental moat, as its value proposition shifts from being the central hub to merely one component in a more fragmented, AI-driven stack. The market's recent sell-off, exemplified by ServiceNow's sharp decline despite strong fundamentals, shows how quickly sentiment can turn against even entrenched platforms when the AI narrative shifts.

Datadog faces a parallel risk in its core observability market. The company's premium pricing power is built on its AI-powered platform being the essential nervous system for IT operations. The counter-narrative is that observability itself could become a commoditized utility, where the value of monitoring tools is reduced to basic data collection and analysis. As AI-native agents become more sophisticated, they might perform these tasks more efficiently and cheaply, eroding Datadog's ability to command a high price per user. This would directly pressure the company's already-stretched margins, which are supported by heavy R&D investment.

The bottom line is that the market is weighing two competing stories. The bullish case sees AI integration as a catalyst for deeper customer relationships and continued consolidation. The bearish case sees AI as a disruptive force that resets long-term growth assumptions and compresses the durable, subscription-led economics that built the software sector's premium. For investors, the targeted upside is a function of which narrative prevails. The risks outlined are not hypothetical; they are the very fears that have driven the sector's deep correction and will determine whether the path back to those triple-digit targets is smooth or fraught with volatility.