Software Bear Market: 2 AI Stocks to Buy Before They Soar 102% and 170%, According to Wall Street Analysts

Analysts are bullish on Datadog and Atlassian as AI features boost product engagement and upsell potential: Arete’s Adam Shepherd raised Datadog’s target to $260 (implying ~102% upside from $129) after Datadog reported Q3 revenue +28% to $886M, RPO +53% to $2.8B and non‑GAAP EPS $0.55 (adjusted EPS CAGR ~19% to 2028, current multiple ~66x). Morgan Stanley’s Keith Weiss set a $320 target for Atlassian (≈170% upside from $118) after Atlassian’s September quarter revenue +21% to $1.4B, RPO +42% to $3.3B, non‑GAAP EPS $1.04, AI‑engaged monthly users +50% to 3.5M and an expected adjusted EPS CAGR ~22% to FY Jun‑2027 (current multiple ~30x); both names face valuation and execution risks despite strong AI tailwinds and material near‑term growth metrics.

Analysis

Market structure: AI-enabled observability (Datadog/DDOG) and work-management (Atlassian/TEAM) are clear winners as enterprises consolidate point tools into platform vendors; DDOG RPO +53% to $2.8B and TEAM RPO +42% to $3.3B confirm demand. Point vendors and standalone monitoring tools risk pricing pressure and churn as buyers prefer integrated AI features, compressing smaller vendors’ margins and accelerating share gains for leaders. Macro signal: software index -24% from its 2025 high suggests capital is available at lower valuations for winners, supporting selective M&A or share gains over 12–24 months.

Risks: Tail risks include AI regulation (data/LLM rules) and a major model failure causing enterprise outages — both low probability but high impact and could reprice multiples by 30–50% in 3–12 months. Time horizons matter: expect earnings/upgrade-driven moves in days/weeks, adoption/RPO cadence to drive direction over quarters, and margin normalization (R&D vs. profitability) to play out across years. Hidden dependencies: reliance on cloud providers and third-party LLMs (cost pass-through risk), and usage-based revenue (DDOG) can amplify churn in a downturn.

Trade implications: Tactical positions should be size-constrained and event-driven: overweight TEAM vs. DDOG on valuation (30x vs. 66x) via a relative pair (long TEAM, short DDOG) to isolate execution risk; target 12–18 month horizon. Options: buy 11–13 month call spreads on TEAM to limit premium (cap upside at analyst target ~$320) and buy protective puts or put spreads on DDOG if initiating longs. Rotate capital from small-cap point-tool names into top-tier SaaS leaders; tighten bond exposure modestly if risk-on tech rally accelerates (corporate spreads could compress 10–30 bps).

Contrarian view: Consensus overweights AI as an automatic revenue multiplier — monetization lags are possible, so current multiple expansions may be premature for DDOG. Conversely, the market may underprice TEAM’s cross-sell runway into non-DevOps departments; a sustained >40% growth in AI-engaged MAUs would validate re-rating. Historical parallel: platform consolidation after cloud pullbacks (2016–2018) favored incumbents that increased enterprise lock-in. Watch for unintended consequences: heavy R&D could push free-cash-flow negative for multiple quarters even as bookings grow.

Key Decisions for Investors

• Establish a 2–3% long position in TEAM (buy stock) on any pullback below $130, target $320 within 12–18 months, set a stop-loss at 25% to limit downside if adoption stalls.
• Initiate a 1–2% opportunistic long in DDOG only on a >=20% pullback from $129 (price < $103), target $260 in 12 months, use a 20% stop-loss due to high 66x earnings multiple and execution risk.
• Implement a pair trade: go long TEAM 2% and short DDOG 1% to capture valuation dispersion and hedge macro/sector moves; rebalance after quarterly RPO/MAU prints.
• Use options to size risk: buy Jan 2027 call spread on TEAM (buy OTM call, sell higher-strike call) sized to 1–2% premium to cap cost; buy 6–12 month protective puts on any DDOG long larger than 1% to cap tail risk.
• Monitor four concrete triggers in next 60–90 days before scaling: (1) quarterly RPO growth >30%, (2) AI-engaged MAU growth >40% (TEAM), (3) gross margin trends after AI rollout, and (4) any regulatory notices on enterprise AI — increase size if 1–3 are met, reduce if regulatory action occurs.