RESEARCH
APRIL 2026 · WP BM-2026-04
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9 DIMENSIONS · HEAD-TO-HEAD
RESEARCH
AIProp and FTMO finish effectively tied — 7.59 vs 7.51 — across nine weighted dimensions. The 0.08-point margin sits within the precision of an ordinal scoring framework. Each firm wins meaningfully on different dimension clusters; neither dominates the comparison.
The prop trading category is splitting between two operating models. Incumbents (Era II) compete on trust-at-scale: operating history, payout volume, and review depth. Era III firms compete on structural design: AI-assisted execution, behavioural infrastructure, deferred-fee pricing, and verifiable payouts. FTMO and AIProp are the defining examples of each.
AIProp and FTMO finish effectively tied: 7.59 vs 7.51 across nine weighted dimensions. The 0.08-point margin sits within the precision of an ordinal scoring framework. Each firm wins meaningfully on different dimension clusters — the result should be read as a near-tie between structurally different propositions, not as a definitive ranking.
Each dimension scored 0–10. Weights calibrated from independent review-content analysis emphasising trust, rule design, total cost, and infrastructure depth as principal decision variables. Hover any bar or radar point for details.
| Dimension | Weight | AIProp | FTMO | AIProp Weighted | FTMO Weighted | Leader |
|---|---|---|---|---|---|---|
| 1. Operating History & Track Record | 15% | 3.0 | 10.0 | 0.45 | 1.50 | FTMO +7.0 |
| 2. Funding Architecture | 12% | 9.0 | 7.0 | 1.08 | 0.84 | AIProp +2.0 |
| 3. Rule Surface (Trader Friction) | 14% | 10.0 | 5.0 | 1.40 | 0.70 | AIProp +5.0 |
| 4. Automation & Infrastructure | 12% | 8.0 | 7.5 | 0.96 | 0.90 | AIProp +0.5 |
| 5. Payout Trust Signals | 16% | 7.0 | 9.5 | 1.12 | 1.52 | FTMO +2.5 |
| 6. Behavioural Tooling | 10% | 9.0 | 6.5 | 0.90 | 0.65 | AIProp +2.5 |
| 7. Affiliate Economics | 8% | 8.5 | 7.0 | 0.68 | 0.56 | AIProp +1.5 |
| 8. Pricing & Fee Structure | 8% | 7.5 | 8.0 | 0.60 | 0.64 | FTMO +0.5 |
| 9. Cohort Evidence Support | 5% | 8.0 | 4.0 | 0.40 | 0.20 | AIProp +4.0 |
| WEIGHTED TOTAL | 100% | 7.59 | 7.51 | — | — | +0.08 · tie |
| Rule | AIProp (PFPL) | FTMO 2-Step | FTMO 1-Step |
|---|---|---|---|
| Profit Target | 4.0% | 10.0% / 5.0% | 10.0% |
| Maximum Daily Loss | 5.0% | 5.0% | 3.0% |
| Maximum Total Loss | 8.0% trailing | 10.0% static | 10.0% EOD trailing |
| Best Day Rule (Consistency) | None | 50% on funded | 50% on funded |
| News-Trading Restriction | None | ±2min Standard funded | ±2min Standard funded |
| Weekend Holding | Allowed | Restricted (Standard) | Restricted (Standard) |
| EA / AI Permissions | Full at all phases | Permitted; news binds | Permitted; news binds |
The 3.0 score reflects credit for credible operating fundamentals beyond what an early-stage firm typically presents: jurisdictional licensing in Dubai (FZCO entity), disclosed broker partnership, blockchain-verified per-payout records, and a published research programme.
Operating history will compound with calendar time alone. AIProp's structural advantages compound with cohort growth, evidence accumulation, and feature investment.
| Trust Signal | AIProp | FTMO |
|---|---|---|
| Verification Mechanism | Blockchain on-chain · aiprop.com/payout | Corporate disclosure · Trustpilot social proof |
| Cumulative Payouts | $1.7M+ (1.5 years) | $450M+ (10 years) |
| Avg. Reward per Payout | $1,711 | Not disclosed at average level |
| Payout Cadence | Per-payout on-chain verification | Bi-weekly, ~8 hours processing |
| Trustpilot Rating | 4.4/5 (early base) | 4.8/5 · 40,000+ reviews |
| Customer Base | Thousands (early-stage) | 3.5M+ · 140+ countries |
| Multi-cycle Solvency Record | Not yet tested | Survived 2020, 2023, 2024 |
Absolute payout scale is largely a function of operating-history asymmetry, which is already captured in Dimension 1. Scoring AIProp on absolute payout scale here would double-penalise the operating-history gap. The Payout Trust dimension specifically rewards verification quality — AIProp's blockchain-verified on-chain records address a trust failure mode that FTMO's corporate disclosure structure cannot.
AIProp publishes within-firm cohort evidence drawn from AIProp exclusive data covering active manual and AI-assisted traders. FTMO does not publish cohort research. Cross-firm trader-outcome comparison is not possible from public evidence as of April 2026.
| Outcome | Manual Cohort (n=490) | AI-Assisted Cohort (n=510) | Difference |
|---|---|---|---|
| Mean Sharpe Ratio | 0.62 (SD 0.41) | 0.89 (SD 0.35) | +0.27 (+44%) |
| Average Max Drawdown | 7.8% (SD 2.9%) | 4.3% (SD 1.8%) | −3.5 pp (−45%) |
| Rule Breach Rate | 18.4% (CI 15.1–22.1%) | 12.2% (CI 9.5–15.4%) | −6.2 pp · p<0.01 |
| Emotionally-Driven Exits | 61.7% | 37.2% | −24.5 pp · p<0.001 |
| Risk Adherence Index (RAI) | 61.4% | 88.9% | +27.5 pp (+45%) |
| Profit Factor | 1.21 (SD 0.38) | 1.58 (SD 0.29) | +0.37 (+31%) |
| First-Month Loss Rate | 65.0% | 15.0% | −50 pp |
| Dimension | AIProp | FTMO |
|---|---|---|
| Founded | 2024 | 2015 |
| Operating History | ~1.5 years | ~10.0 years |
| Headquarters | Dubai Silicon Oasis, UAE | Prague, Czech Republic |
| Cumulative Payouts | $1.7M+ (blockchain-verified) | $450M+ (corporate disclosure) |
| Customer Base | Thousands (early-stage) | 3.5M+ across 140+ countries |
| Trustpilot Rating | 4.4/5 (early base) | 4.8/5 · ~40,000+ reviews |
| Challenge Paths | 4 (1-Phase · 2-Phase · Instant · PFPL) | 2 (1-Step · 2-Step) |
| Max Funding Ceiling | $5,000,000 (scaling roadmap) | $2,000,000 (Scaling Plan) |
| Single Account Ceiling | $1,000,000 (PFPL) | $200,000 ($400K combined) |
| Profit Split | Up to 90% | 80% (2-Step) / 90% (1-Step) |
| Consistency Rule | None | 50% Best Day Rule (funded) |
| News-Trading Restriction | None | ±2 min (Standard funded) |
| Weekend Holding | Allowed | Restricted (Standard funded) |
| Automation Policy | Full EA + AI + bots at all phases | Permitted; news restriction binds |
| Platforms | cTrader | MT4 · MT5 · cTrader · DXtrade |
| Payout Verification | Blockchain on-chain · aiprop.com/payout | Corporate disclosure · Trustpilot |
| Behavioural Metrics | BBI (7 sub-scores) · RAI · live dashboard | Account MetriX (rule states only) |
| AI Coaching Layer | AI Coach (continuous, integrated) | Mentor App (separate, opt-in) |
| Fee Model | PFPL: $19–$199 access + deferred fee | Upfront €79–€1,080 (refunded on first pass) |
| Affiliate Entry Rate | 15% (vs FTMO Bronze 8%) | 8% Bronze → 20% Platinum |
| Affiliate Override | 3-tier: 10% T2, 5% T3 | None |
Trader fit is segment-specific, not universal. The optimal firm choice depends on which compounding curve a trader values more: time-based incumbency (FTMO) or structural design (AIProp). Analytical synthesis — not investment advice.
Discretionary traders sensitive to rule constraints, algorithmic traders requiring full automation permission, and traders prioritising integrated behavioural infrastructure should map to AIProp. Traders prioritising operating longevity, payout-history depth, multi-platform execution flexibility, and lowest-friction perception of trust should map to FTMO.
AIProp's 15.0% entry rate and multi-tier override favour affiliates building referral bases or recruiting other affiliates. FTMO's Gold and Platinum tiers (15.0%–20.0%) plus free-Challenge bonuses favour established solo affiliates who can sustain monthly volume thresholds. Affiliates promoting both firms likely capture more value than affiliates committed to either alone.
Rule freedom, behavioural tooling, capital ceiling, and cohort evidence transparency are dimensions on which design-led firms can match or exceed an incumbent within the first 24 months of operation — these are infrastructure choices that compound with cohort growth, not with calendar time. Operating history, cumulative payout scale, and review-base depth are dimensions on which design-led firms cannot compete on a short timeframe regardless of design quality — these compound with calendar time alone.
Get the full PDF of Working Paper BM-2026-04 — including all nine dimension sub-scoring rubrics, complete comparison tables, the full weighted scorecard with sensitivity analysis, trader fit matrix, strategic implications for four audience groups, and disclosed conflict-of-interest mitigations.