This Artificial Intelligence (AI) Stock Is Down Around 30% This Year. Could It Be a Steal of a Deal?

UiPath reported fiscal year revenue of $1.6B (up 13% y/y) and turned a net profit of $282.3M versus a $73.7M loss a year earlier. The stock has fallen ~30% year-to-date and analysts have been cutting targets; the consensus price target is just over $14 (~20% implied upside). The article cites intense competition and lackluster growth as reasons investors remain skeptical and suggests there is not a compelling case to buy the stock today.

Analysis

Enterprise RPA vendors are now fighting two structural forces at once: consolidation of buyer stacks around hyperscalers and rapid, low-friction substitution from open-source/agentic AI frameworks. That creates a margin squeeze not just from pricing but from a shift in implementation economics — professional services and systems integrators will capture more of the spend, compressing software take-rates unless vendors win platform-level hooks. NVDA and other infra players gain indirectly as customers trade monolithic attended-automation projects for inference-heavy agent deployments that consume GPU cycles at higher unit economics.

Near-term (weeks–quarters) the dominant risks are execution and churn: missed big-logo renewals or a single large competitive deal loss will mechanically depress guidance and multiple compression. Over 6–24 months the bigger tail is commoditization of the RPA layer into orchestration primitives offered by cloud vendors or embedded in agent stacks, which would convert high-margin recurring revenue into low-margin services. Reversals are straightforward to monitor — sustained large-logo dollar-based net retention expansion, hyperscaler co-sell agreements, or a strategic M&A bid would force a rapid re-rating.

Practical positioning should express conviction that RPA is a second-order AI loser while remaining mindful of asymmetric upside if fundamentals re-accelerate. Use a paired trade to short idiosyncratic exposure to execution risk while taking leveraged, directional exposure to the AI infra winners that capture spend flowing away from legacy automation. For contrarian, size any long PATH exposure small and time it to a 6–12 month catalyst (renewal season or strategic announcement), using options to cap premium paid.

Key Decisions for Investors

• Pair trade (6–12 months): Short PATH equity sized 1–2% NAV and use proceeds to buy NVDA calls (delta ~0.6 equivalent exposure) of same notional. Rationale: expresses view that AI infra (NVDA) captures structural spend while PATH faces execution/competitive risk; target 40–50% downside in PATH vs 2–3x upside in NVDA if AI spend continues. Stop-loss: PATH +20% or NVDA -30% for asymmetric risk control.
• Options hedge (3–9 months): Buy a PATH put spread (buy 6–9 month OTM put, sell cheaper deeper OTM put) sized to hedge 0.5–1% NAV of software long exposure. Risk/reward: limits premium outlay while capturing downside if quarter misses; max loss = premium paid, max gain sized by spread width if PATH collapses.
• Event play (6–12 months, contrarian small position): Sell a modest number of cash-secured PATH puts at a strike ~15–20% below current price for 6–12 months to accumulate shares if severely repriced, size <=0.5% NAV. Rationale: if market has over-penalized durable recurring revenue and margins continue improving, effective entry will be attractive; downside is assignment at that strike, so capital must be reserved.
• Tactical allocation tilt (12+ months): Overweight NVDA vs INTC at the margin within AI exposure bucket — NVDA for near-term inference capture, INTC as a tactical hedge if valuation mean-reverts. Use call spreads on NVDA (6–12 month) to limit premium and pair with a small allocation to INTC cash or long-dated options for optionality; reward biased to NVDA upside with defined downside on option cost.