With Sift Stack, two ex-SpaceX engineers are bringing the software that helped launch rockets to the factory floor

$42 million Series B raised in 2025 at a $274 million post-money valuation. Sift Stack is pivoting to prioritize data infrastructure as AI-driven analysis makes machine-readable telemetry critical—some vehicles stream >1.5 million concurrent sensors—and the company positions data plumbing as the enabler for automated manufacturing. Customers (e.g., Astranis) report workloads like ~10 million automated tests/day and storage costs in the "millions of dollars per month," highlighting commercial demand amid broader industry interest (including reports of a $100 billion Bezos fund to automate factories).

Analysis

The important read-through here is that the bottleneck for 'atoms, not bits' is increasingly data plumbing, not robot arms. Manufacturers will reallocate at least a mid-single-digit percent of their automation capex toward storage, time-series indexation and AI-ready ETL over the next 3 years — that reallocation compounds because storing high-fidelity telemetry is a recurring monthly cost rather than a one-time install, creating durable revenue for whoever owns the ingestion + query layer.

Hyperscalers have a structural advantage: they can bundle low-cost object storage, specialized time-series engines and model hosting while cross-subsidizing margins with broader AI services, making it very capital-efficient to win industrial telemetry. The second-order consequence is accelerant M&A into niche data-infrastructure firms (acqui-hire for IP + customers) over 12–36 months, which benefits both acquirers (faster go-to-market) and asset managers seeding those startups, creating a private-market carry tailwind.

Key near-term risks are macro-driven manufacturing slowdowns (capex pause within 0–12 months) and regulatory segmentation of defense/critical infrastructure telemetry that forces on-prem deployments, curbing cloud capture. A reversal catalyst would be rapid commoditization of ML inference at the edge or meaningful declines in cloud ingress/egress economics, which would reroute spend away from centralized providers.

Consensus is underestimating complexity: not all telemetry equals cloud revenue — high-frequency deterministic control and regulatory constraints will favor hybrid architectures and specialized vendors. Track telemetry ingestion growth, industrial cloud billings, and fundraising pace for industrial-AI funds as leading indicators for when this market shifts from nascent to incumbents-winning.

Key Decisions for Investors

• Buy GOOG (Alphabet) exposure via a 12-month call spread to capture industrial-cloud monetization: size 1.5–2% NAV (long near-ATM 12-month call, short a higher strike to finance). Target 20–30% upside if industrial AI increases cloud spend; max loss = premium paid. Hedge with a 6–12 month protective put if macro risk rises.
• Initiate a small long position in STEP (StepStone) — 1% NAV, 6–12 month horizon — as a play on private-market fee/carry tailwinds from industrial-AI fundraising. Objective +15–25% if fundraising and exits accelerate; downside -20% in a public markets drawdown or if fundraising stalls.
• Event-driven alert and optional pair: if we see >2 announced M&A deals of industrial-data startups by hyperscalers within 12 months, add to GOOG position and trim cyclical industrial exposure. Conversely, if regulatory segmentation (data localization rules) materializes in key jurisdictions, rotate toward niche hybrid/cloud vendors and reduce GOOG exposure.