New Rules for New Models: Beyond ARR in the AI Era
AI startups are pushing boundaries in ways traditional SaaS metrics struggle to capture—and yet, when it comes time to raise capital, Annual Recurring Revenue (ARR) is still the gold standard.
Annual Recurring Revenue (ARR) has become the default shorthand for traction and fundraising readiness in venture capital. It’s simple, standardized, and easy to benchmark across companies. For B2B SaaS startups with predictable subscription models, it works well.
For investors, it signals predictable income, product-market fit, and sales efficiency. For founders, ARR often marks the gateway to Series A or B funding.
But for AI startups, ARR is an increasingly imperfect tool.
This article explores why ARR gained prominence, where it falls short for modern AI companies, and what new metrics could better capture the value these startups create.
Why ARR Became the Standard
ARR rose to dominance during the SaaS boom for a few good reasons:
Predictability: Investors love consistency. ARR offered a stable, forward-looking metric tied to subscription revenue.
Comparability: ARR enabled apples-to-apples benchmarking across SaaS startups.
Growth Efficiency: Paired with metrics like CAC and NRR, ARR helped evaluate go-to-market effectiveness.
Valuation Simplicity: Revenue multiples provided a shorthand for pricing rounds.
For traditional SaaS businesses, it remains a powerful and relevant measure. But AI businesses aren’t traditional.
Where ARR Falls Short for AI Startups
Many investors are privately voicing frustration with ARR’s limits in today’s market. It no longer tells the full story. Some of the most strategically valuable startups aren’t optimized for ARR in their early years—and that’s not necessarily a flaw in the business, it’s a reflection of how quickly the model is evolving.
AI companies operate under different economics, delivery models, and development cycles:
Usage-Based Monetization: Many AI tools monetize via API calls, tokens, or compute usage. Spiky and variable usage patterns don’t align neatly with recurring revenue.
High R&D Load: Foundational model development and fine-tuning are capital intensive.
Pilot-Heavy GTM: Many AI startups rely on pilots or co-development engagements. These don’t always translate into ARR, but they signal strategic traction.
IP as a Differentiator: AI companies often compete on proprietary models, datasets, or infrastructure—none of which are captured by ARR.
ARR isn’t obsolete—but for AI companies, it’s incomplete.
Metrics to Consider as Complements or Alternatives to ARR
Rather than discard ARR entirely, I propose supplementing it with new metrics tailored to the realities of AI businesses. These are especially relevant for early-stage companies where usage, traction, and ecosystem integration matter more than revenue.
Here are some metrics that could help investors and founders better understand progress and potential in AI-native startups:
1. Pilot-to-Production Ratio
Measures how many pilot deployments convert to full-scale implementations. A proxy for product-market fit in enterprise AI.
2. Model Usage Value (MUV)
Revenue per token or inference request. Reflects monetization efficiency and can help benchmark across usage-based models.
3. Gross Margin per Inference
Highlights profitability at a unit level—critical for evaluating scalability and cost control.
4. Embedded Revenue Potential (ERP)
Forward-looking estimate of revenue from deep integration into enterprise workflows. Especially useful for startups selling AI copilots or productivity tools.
5. AI-Adjusted Net Retention (ANR)
Modified NRR to account for usage variability and model iteration cycles.
6. Model Improvement Velocity (MIV)
How quickly a model improves over time. Could include accuracy, latency, or cost-per-inference gains.
7. Data Flywheel Index (DFI)
Quantifies how new data accelerates model performance. A proxy for long-term defensibility.
8. Strategic Integration Score (SIS)
Tracks depth of integration into customer systems. High SIS may indicate strong switching costs and long-term alignment.
9. AI Co-Pilot Engagement Rate
For application-layer AI startups, measures frequency and quality of user interaction with generative or assistive agents.
10. Time to Strategic Co-Development
Evaluates how quickly an AI startup enters meaningful co-development with enterprise partners.
Not All AI Startups Are Alike
These metrics won’t apply uniformly. Infrastructure startups, application-layer tools, and vertical AI solutions all monetize differently. Founders and investors should be selective and intentional—choosing the metrics that best reflect the business model and stage.
What Comes Next: Driving Adoption and Communication
To gain traction, these metrics must be:
Simple to explain to investors.
Tied to business value and long-term outcomes.
Consistently tracked across comparable businesses.
Startups should introduce these metrics early in conversations with investors—especially in data rooms, board decks, and investor updates. If CVCs, forward-looking VCs, and strategic acquirers begin referencing these KPIs, standardization will follow.
We’re not proposing a wholesale replacement of ARR. But it’s time we acknowledged its limits—and began building a broader language for evaluating today’s most innovative startups.
By evolving how we measure traction, we can improve investor communication, accelerate alignment, and unlock better strategic partnerships. ARR will still matter—but it shouldn’t be the only thing that does.