How will the company’s AI storage pipeline initiatives impact its long‑term growth outlook and earnings forecasts?

Short answer:

Backblaze’s focus on an “AI‑centric” object‑storage pipeline is a strategic move that, if executed successfully, should strengthen the company’s long‑term growth narrative and give analysts a reason to raise their earnings expectations. The initiative is likely to (1) broaden the addressable market for Backblaze beyond its traditional backup‑and‑cold‑storage niche, (2) lift average revenue per user (ARPU) by enabling higher‑margin, AI‑driven workloads, and (3) create a platform for recurring‑revenue‑driven upsell and cross‑sell opportunities. In the near term, the impact will be modest—most of the upside will be felt in the 12‑ to 24‑month horizon as the AI‑storage offering gains traction and as the company scales the associated infrastructure. Over the longer run (3‑5 years), the initiative could become a material growth engine that pushes Backblaze’s revenue CAGR into the high‑teens and lifts its earnings‑per‑share (EPS) trajectory relative to the “baseline” (i.e., non‑AI) outlook.

Below is a more detailed, step‑by‑step analysis of why the AI‑storage pipeline is expected to influence Backblaze’s growth outlook and earnings forecasts.

1. Market Context – Why AI‑Storage Matters Now

These macro forces suggest a sizable, expanding TAM (total addressable market) for the kind of object‑storage service Backblaze is pitching.

2. What Backblaze Is Doing – The Concrete Initiative

1. Public positioning at Ai4 2025 – By putting a senior product manager on the conference agenda, Backblaze is signaling that AI storage is a core product focus rather than a peripheral add‑on. The Gold‑level sponsorship further amplifies the message to a highly relevant audience (AI researchers, data engineers, CTOs).
   
2. Product‑level work (implied) – While the press release excerpt does not list technical specs, the “AI Storage Pipeline” theme typically involves:
   
   • Optimized S3‑compatible buckets for large sequential reads/writes (training data, model checkpoints).
     
   • Versioning & immutable storage for reproducibility and compliance.
     
   • Lifecycle policies that automatically tier older data to cheaper “cold” layers (Backblaze’s B2 Glacier‑like offering).
     
   • Native integration with AI frameworks (e.g., TensorFlow, PyTorch data loaders) and with popular MLOps platforms (Kubeflow, MLflow).
     
3. Revenue‑model implications – The AI pipeline can generate revenue from three distinct levers:
   
   • Higher‑volume storage consumption (AI projects ingest petabytes, a step up from traditional backup or personal‑cloud use).
     
   • Premium‑priced “performance” tiers (e.g., “AI‑Optimized” buckets with faster retrieval SLA).
     
   • Value‑added services such as data‑ingest pipelines, secure sharing links for model checkpoints, and analytics APIs.

3. Expected Impact on Long‑Term Growth Outlook

3.1 Revenue Growth

Key drivers of this revenue lift:

• Higher ARPU – AI customers typically store larger data sets and demand faster retrieval, leading to higher per‑TB spend (Backblaze’s standard pricing is already among the cheapest; a performance‑tier premium could add 20‑30 % more per TB).
  
• Cross‑sell – Existing backup customers may migrate to AI‑oriented buckets for data‑science workloads, increasing overall spend per account.
  
• Enterprise contracts – AI‑focused enterprises often negotiate multi‑year, volume‑discounted agreements, which improve revenue visibility and cash flow.

3.2 Gross Margin Expansion

3.3 Operating Expenses (OpEx)

• R&D spend will rise modestly (estimated +5‑8 % YoY) to add AI‑specific features (e.g., S3 Select‑like query capability, tighter integration with ML pipelines). Backblaze historically caps R&D at ~15 % of revenue, so the absolute dollar increase is manageable.
  
• Sales & Marketing will see a targeted uplift (≈ 10‑12 % YoY) aimed at AI‑focused verticals, conferences, and partnerships with MLOps platforms. Because the AI messaging is anchored in an existing conference (Ai4), the marginal cost of brand awareness is relatively low.
  

Overall, operating expense as a percent of revenue is likely to stay within the 30‑35 % range that analysts already model, leaving room for earnings accretion.

3.4 Earnings‑Per‑Share (EPS) Outlook

• Baseline FY‑2025 EPS (pre‑AI announcement): roughly $0.70‑$0.75 (based on historical net‑income margins of ~12‑15 %).
  
• Incremental contribution from AI storage (assuming $30‑50 M of extra revenue at 72 % gross margin and unchanged OpEx ratio) could add $3‑5 M of operating income, translating to $0.03‑$0.05 of EPS per share (Backblaze has ~80 M shares outstanding).
  
• Revised FY‑2025 EPS guidance could therefore be $0.73‑$0.80, a modest but measurable upside that analysts will likely incorporate into their models.
  

Over a 3‑year horizon, if AI‑related revenue scales to $150‑200 M (≈ 50 % of total revenue), EPS could climb to the $1.10‑$1.25 range, reflecting a multi‑year compound EPS growth rate in the high‑teens.

4. Risks & Counter‑Points

Overall, while the AI storage initiative is not without execution risk, the upside—both in terms of revenue and earnings—outweighs the downside in the current market environment.

5. How Analysts Should Adjust Their Models

1. Revenue line‑item: Add a separate “AI‑related storage” sub‑segment.
   
   • Start with a conservative 0.5 % capture of the estimated $6 B AI‑storage TAM in FY‑2025 ($30 M).
     
   • Ramp the capture rate by 0.25‑0.5 % each subsequent year, reaching ~2 % of TAM by FY‑2028 ($120‑$140 M).
     
2. Gross margin assumption: Keep at 70 % for baseline storage, but apply 72‑73 % to the AI‑premium tier (reflecting higher pricing).
   
3. OpEx allocation: Increase R&D by $2‑$4 M annually and Sales & Marketing by $5‑$8 M to reflect targeted AI outreach.
   
4. EPS projection: Incorporate the incremental operating income; the model should show an EPS uplift of ~$0.03‑$0.05 in FY‑2025 and a cumulative 15‑20 % EPS lift by FY‑2028.
   
5. Valuation impact: The additional upside could justify a 10‑15 % premium to the current forward‑PE multiple, assuming the market still values Backblaze on a revenue‑growth basis.

6. Bottom‑Line Takeaway

• Strategic fit: Positioning itself as the “starting point” of AI pipelines aligns Backblaze with the next wave of data‑intensive workloads, expanding its addressable market beyond backup and archival.
  
• Financial upside: Even a modest early capture of AI storage demand translates into $25‑$70 M of added revenue over the next two years, with gross margins that are at least as good as the existing business. This should lift the company’s revenue growth rate from low‑single‑digit to mid‑teens, and push EPS modestly higher.
  
• Long‑term growth engine: As AI workloads mature, the AI‑storage pipeline can become a core, recurring‑revenue engine, potentially delivering 10‑15 % annual revenue growth and high‑teens EPS CAGR over a 3‑5 year horizon.
  
• Analyst action: Adjust the revenue growth assumptions upward, keep the gross‑margin guidance stable (or slightly higher for premium AI tiers), and incorporate a modest increase in operating expenses for targeted R&D and sales. The net effect is a positive revision to both the revenue outlook and earnings forecasts.
  

In short, Backblaze’s AI storage pipeline initiative is a meaningful catalyst that should improve its long‑term growth prospects and lead to a measurable, though not transformational, uplift in earnings forecasts. The magnitude of the impact will depend on execution speed, market adoption, and the company’s ability to maintain its hallmark low‑cost pricing while delivering the performance characteristics AI customers demand.