Thesis: programmatic usage → credit consumption → revenue. Credits are consumed in two categories: AI tokens (chat completions, agent reasoning) and query execution (SQL seconds). Across 21 external orgs, 86% of all credits come from programmatic access (API, CLI, automated pipelines) — not from users clicking around in the UI. If we want to grow revenue, we need to invest in product that drives programmatic usage.
| Signal | Classification | What It Means |
|---|---|---|
session_id = NULL | Programmatic — Direct API | Query or AI call made outside any chat session. Pure API/SDK usage. |
meta.source = “cli” | “api” | Programmatic — CLI/MCP | Chat session initiated from CLI, Cursor, or MCP tool. Has a session, but not human-in-the-UI. |
meta.source = “web” | “agents-page” | Manual — Web UI | User in the browser, chatting with agents or running queries manually. |
| Burst queries (<2s gaps) | Programmatic — Pipeline | Rapid-fire queries at machine speed. Marinade: 90% burst rate. CMC: 98%. |
session_id.
After incorporating meta.source, his classification moved to 97.7% programmatic (233 of 298 sessions are CLI-originated,
plus all sessionless query events). This confirmed the heuristic works.
| Pattern | Orgs | % of Credits | Programmatic % | What They Do |
|---|---|---|---|---|
| API Pipeline | 3 | 80% | ~100% | Automated SQL pipelines via API. Machine-speed burst queries. Zero AI. High volume, low stickiness. |
| AI Chat | 6 | 13% | ~15% | Exploring data through agents in the UI. Human-paced. High AI %, low query volume. Undermonetized. |
| Mixed | 3 | 11% | ~60% | Both AI exploration and query execution. Closest to the target pattern. Highest stickiness potential. |
Credit consumption follows a clear hierarchy. Each step up the programmatic ladder multiplies revenue per user:
| Access Pattern | Avg Credits/Day | Rev Multiplier | Why |
|---|---|---|---|
| Manual UI only | 50–100 | 1x | Browsing, occasional chat. Human-speed ceiling caps consumption. |
| Manual chat + API queries | 200–350 | 3–5x | Chat for exploration, then direct API for extraction. Eric Stone’s pattern. |
| Agent-driven (CLI/Cursor/MCP) | 300–500 | 5–10x | One agent session triggers 3–10 downstream queries that iterate and self-correct. |
| Automated pipeline (API) | 1,800–6,000 | 20–60x | Machines run 24/7 at burst speed. No human bottleneck. |
| Organization | Credits | AI % | Qry % | Cred/Day | Est Rev/Mo | Pattern |
|---|---|---|---|---|---|---|
| Marinade Finance | 64,025 | 3% | 97% | 1,829 | $1,646 | Pipeline |
| CMC Capital SRL | 20,817 | 0% | 100% | 5,204 | $4,684 | Pipeline |
| Stockholm SSE | 18,066 | 0% | 100% | 6,022 | $5,420 | Pipeline |
| CoW Swap | 6,922 | 36% | 64% | 865 | $779 | Mixed |
| Laminated Labs | 6,242 | 91% | 9% | 223 | $201 | AI Chat |
| Near Foundation | 5,879 | 31% | 69% | 210 | $189 | Mixed |
| Avalanche | 3,591 | 81% | 19% | 257 | $231 | AI Chat |
| Monad | 2,684 | 71% | 29% | 112 | $101 | AI Chat |
| Aleo | 2,631 | 86% | 14% | 329 | $296 | AI Chat |
| Somnia | 1,302 | 74% | 26% | 93 | $84 | AI Chat |
| Karatage | 1,228 | 34% | 66% | 123 | $111 | Mixed |
| Flow | 824 | 16% | 84% | 59 | $53 | Light |
| Attestant | 628 | 88% | 12% | 45 | $41 | AI Chat |
| + 8 orgs under 500 credits | Circle, Solana, Ink, Polygon, Sui, Morpho, Kraken, Movement | |||||
The ideal customer uses AI agents to explore data and runs queries to extract it. Query-only users are high-volume but commoditized (any SQL warehouse will do). AI-only users are low-volume and substitutable (ChatGPT + a SQL tool). When both layers are engaged, AI drives queries and queries feed AI — a flywheel that’s hard to leave.
Two internal power users with completely different workflows independently converged on similar consumption patterns. This gives us the per-user building block — the question is how many users per org we can drive.
| Jim Myers — “Agent-First” | Eric Stone — “Explorer” | |
|---|---|---|
| AI : Query ratio | 52 : 48 | 44 : 56 |
| Credits / day | ~500 | 326 |
| Monthly projection | ~15,000 ($450) | ~9,800 ($294) |
| Workflow | CLI agents trigger cascading queries | Chat exploration, then direct API queries |
| Channel | 78% CLI/Cursor, 49% no-session queries | 11 chat sessions, 105 direct API queries |
| Input | Value | Basis |
|---|---|---|
| Credits per user per day | 300–500 | Eric = 326, Jim = 500. Conservative: 350. |
| AI : Query ratio | 40 : 60 | Eric = 44:56, Jim = 52:48. Midpoint. |
| Active days per month | 22 | Weekdays |
| Revenue per user per month | $198–$330 | 6,600–11,000 credits × $0.03 |
A single user at $198–$330/month isn’t a business. Healthy means an org worth building for — one that justifies the $10M ARR ambition. That requires multiple users per org, both AI and query usage, and ideally a mix of programmatic and agent-driven access patterns.
| Org Stage | Users | Cred/U/Day | Credits/Mo | Rev/Mo | Rev/Year | Health |
|---|---|---|---|---|---|---|
| Early — single user | 1 | 300 | 6,600 | $198 | $2,376 | Not yet healthy. Need to expand. |
| Growing — small team | 3 | 350 | 23,100 | $693 | $8,316 | Getting there. Team workflows forming. |
| Healthy — data team | 5 | 350 | 38,500 | $1,155 | $13,860 | Healthy. Pipelines + agents + analysts. |
| Strong — power team | 10 | 450 | 99,000 | $2,970 | $35,640 | Strong. Multiple workflows, org-wide. |
| Enterprise — scaled | 20 | 500 | 220,000 | $6,600 | $79,200 | Enterprise. Deep integration across teams. |
Margins are identical (80%) on AI and query credits. The 40:60 target is about stickiness and moat, not profitability per unit. More AI usage compounds revenue — one agent session at ~$0.75 of AI credits triggers ~$1.20 of query credits.
Product drives adoption and expansion. Sales closes the largest accounts. This is the most realistic path given where we are today — 13 orgs, no sales team, strong product but early traction.
| Org Cohort | Count | Avg ARPU | Rev/Mo | % of Rev | Who These Are |
|---|---|---|---|---|---|
| Small (early / single user) | 300 | $250 | $75K | 9% | New sign-ups, exploring. The top of the funnel. |
| Mid (growing teams) | 150 | $1,500 | $225K | 27% | 3–5 users, daily workflows. Expanding. |
| Large (healthy+) | 60 | $4,000 | $240K | 29% | 10+ users, pipelines + agents. The target org. |
| Enterprise (sales-assisted) | 50 | $6,000 | $300K | 36% | 20+ users, deep integration. Sales closes these. |
| Total | 560 | $1,500 | $840K | 100% | |
| ARR | $10.1M | GP: $8.1M at 80% margin |
The question isn’t just “how many new orgs?” — it’s “how fast do existing orgs expand?” If orgs grow from $250 → $1,200 → $3,500/month over 12–18 months as they add users and workflows, net revenue retention exceeds 150%. That expansion compounds — 80 orgs at $3,500/month is $280K/month alone.
| Stage | Users | Credits/Mo | Rev/Mo | What Changes |
|---|---|---|---|---|
| Month 1 — Sign up | 1 | 4,400 | $132 | First user explores with AI, runs a few queries |
| Month 3 — Add teammate | 2 | 13,200 | $396 | Second analyst joins, daily workflows forming |
| Month 6 — Data team | 5 | 38,500 | $1,155 | Team pipelines + agents running daily |
| Month 12 — Power team | 10 | 99,000 | $2,970 | Org-wide: pipelines + agents + analysts |
| Month 18 — Scaled | 20 | 220,000 | $6,600 | Full adoption, 20+ active users |
Today’s evidence: Marinade is already at $1,646/month (54,870 credits). CMC Capital hit $4,680/month projected rate in just 4 days. Stockholm SSE hit $5,420/month projected. The spend levels are real — the question is whether we can get these orgs into the agent layer (currently 0–3% AI) to make the revenue sticky and expand it.
| Orgs | Avg ARPU | Rev/Mo | ARR | AI % | What Has to Be True | |
|---|---|---|---|---|---|---|
| Today Feb ’26 | 13 | $196 | $2,550 | $31K | 29% | Starting point. 3 pipeline orgs carry 80% of credits. 9 orgs dormant. |
| Month 2 Apr ’26 | 18 | $228 | $4,100 | $49K | 33% | Activate 3–4 dormant orgs. Push AI adoption on Marinade/CMC/Stockholm. 1–2 net new orgs. ARPU stays low as new orgs ramp. |
| Month 4 Jun ’26 | 24 | $317 | $7,600 | $91K | 37% | Pipeline orgs starting to use agents. Early orgs deepening — first org hits $1K/mo. Adding 3/month net new. |
| Month 6 Aug ’26 | 30 | $416 | $12.5K | $150K | 40% | Validation checkpoint. Dormant orgs activated. Pipeline orgs at 15%+ AI. 3–5 orgs above $500/mo. Expansion signal visible. |
| Month 9 Nov ’26 | 55 | $680 | $37.4K | $449K | 41% | Adding ~8 orgs/month. Early cohorts expanding — 5–8 orgs above $1K/mo. ARPU inflecting as expansion kicks in. |
| Month 12 Feb ’27 | 100 | $1,040 | $104K | $1.25M | 42% | Scale checkpoint. Adding ~12 orgs/month. 15+ orgs above $1K/mo. First $6K+ accounts. NRR > 130%. |
| Month 15 May ’27 | 200 | $1,125 | $225K | $2.7M | 43% | Adding ~30 orgs/month. Expansion engine running. Multiple orgs at $3K–6K. Dedicated sales for enterprise tier. |
| Month 18 Aug ’27 | 350 | $1,190 | $417K | $5M | 44% | Adding ~40 orgs/month. 30+ orgs above $3K/mo. Enterprise pipeline building. NRR > 150%. |
| Month 21 Nov ’27 | 530 | $1,200 | $636K | $7.6M | 44% | Adding ~50 orgs/month. Portfolio maturing — older cohorts at $3.5K+ pulling avg ARPU up. |
| Month 24 Feb ’28 | 710 | $1,211 | $860K | $10.3M | 45% | Target. ~80 orgs at $3.5K+, ~200 at $1.2K, ~400 at $250. ~30 enterprise at $6K+. Adding ~60 orgs/month. |
The thesis says programmatic usage drives revenue. The playbook follows: prioritize product investments that move users up the programmatic ladder, ranked by expected credit impact.
Build: Make it seamless for an agent session to trigger, iterate, and schedule follow-up queries.
Today an agent session generates ~$0.75 in AI credits and ~$1.20 in query credits.
If that query tail extends (scheduled refreshes, automated pipelines spawned from chat), each session becomes a recurring revenue stream.
Impact: Moves AI Chat orgs (6 orgs, 13% of credits, 79% AI) toward balanced consumption.
Laminated, Avalanche, Monad, Aleo are all exploring with agents but never cross into query execution. This is the bridge.
Ship: “Save as scheduled query” from chat, agent-initiated query pipelines, one-click automation from results.
Build: First-class API/SDK/CLI for running queries and invoking agents programmatically.
The three pipeline orgs (Marinade, CMC, Stockholm) already hit $1.6K–$5.4K/month through raw API access alone —
but with zero AI because there’s no good way to invoke agents from a pipeline.
Impact: +$1,957/mo immediately if pipeline orgs reach 40% AI at current query volume.
Also unlocks the highest-spend tier for every new org — a team with API access spends 20–60x more than a UI-only user.
Ship: Agent API endpoints, Python/JS SDKs, CLI agent invocation, MCP server improvements, webhook triggers.
Build: Make it trivial to add teammates to an org and get them producing value on day one.
Today most orgs have 1–2 users. Healthy is 5–10. Every additional user at $198–$330/month is linear ARPU growth.
Impact: Moving avg org from 1.5 users to 5 users triples ARPU with no acquisition cost.
9 dormant orgs (43% of base) never activated — likely an onboarding problem, not a product problem.
Ship: Team invite flow, shared dashboards/queries, org-level billing visibility, “first query in 5 minutes” onboarding.
Build: Features that generate credits on a recurring basis without human action.
Scheduled queries, automated alerts, embedded dashboards that refresh, agents that run on a cron.
Impact: Transforms one-time exploration into ongoing baseline consumption.
This is what makes NRR > 120% — orgs consume credits even when nobody is at their desk.
Ship: Scheduled query execution, alert thresholds on query results, embeddable dashboards, agent scheduling.
| Risk | Why It Matters | How to Validate |
|---|---|---|
| Pipeline orgs churn | 80% of credits come from 3 orgs running raw SQL. They have zero switching cost — any SQL warehouse works. If one churns, revenue drops 25%+ overnight. | Get pipeline orgs into the agent layer. AI usage = switching cost. Track AI % per pipeline org monthly. |
| Per-user economics don’t replicate | 300–500 credits/day is validated on 2 internal power users. External users may consume less, use AI differently, or not adopt agents at all. | Instrument per-user daily credit consumption for external orgs. Need 10+ external data points in 3 months. |
| Orgs don’t expand | The model assumes orgs grow from 1 to 5–10 users. If most orgs stay at 1–2 users, avg ARPU stays at $200–400 and $10M requires 2,000–4,000 orgs. | Track users-per-org and ARPU monthly. If no expansion signal by month 6, reassess the target. |
| Burst usage isn’t sustained | CMC Capital and Stockholm SSE consumed credits in 3–4 day bursts. Monthly projections assume sustained usage. Actual may be 50–70% lower. | Wait for 60-day observation window. Don’t plan around burst projections until confirmed. |
| Acquisition engine doesn’t exist yet | Going from 13 to 710 orgs in 24 months requires adding ~30 orgs/month average. Today there’s no scalable acquisition channel. | Identify top-of-funnel channel by month 4. Test: content, partnerships, PLG viral loops, outbound. Need 5+ orgs/month by month 6. |
| Metric | Today | 6-Month Target | Why It Matters |
|---|---|---|---|
| AI % of total credits | 29% | 40% | Moat. When this rises, users are in the agent layer. |
| Avg ARPU / org / month | $196 | $500+ | Expansion. Are orgs growing spend via seats and usage? |
| Orgs > $1K/month | 3 | 10 | Power law. High-ARPU orgs drive the majority of revenue. |
| Orgs with both AI & Query >10% | 7 | 20 | Flywheel health. Balanced consumption = sticky. |
| Net Revenue Retention (monthly cohort) | — | >120% | The metric. If existing orgs expand, you win. Track monthly. |
| Pipeline orgs using agents | 0 / 3 | 3 / 3 | Expansion. Biggest revenue lever, no new sales needed. |
| First-query activation (30-day) | 57% | 85% | Funnel. 43% of orgs never activated. |
OrgUsageEvent table via internal-cli -e prod admin usage-events.
21 external organizations, Flipside internal org (538,644 credits) excluded.category field: ai_usage (chat completions) or query_execution (SQL execution).
Credits are calculated by the usage event service based on token counts and query seconds respectively.session_id = NULL → Direct API (no chat session involved)ChatSession.meta.source = "cli" | "api" → CLI/MCP-drivenChatSession.meta.source = "web" | "agents-page" → Web UIsession_id presence/absence.