> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sleuthintel.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Sleuth - Simulation Series - 6 Sims

> Original Sleuth document.

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24px;display:flex;gap:2px;}\n.tabbtn{background:none;border:none;color:var(--green-dim);font-family:'Share Tech Mono',monospace;\n  font-size:0.72rem;letter-spacing:1.2px;padding:16px 14px;cursor:pointer;border-bottom:2px solid transparent;\n  white-space:nowrap;transition:color .15s,border-color .15s;}\n.tabbtn:hover{color:var(--green);}\n.tabbtn.active{color:var(--gold);border-bottom-color:var(--gold);}\n.tabpanel{display:none;}\n.tabpanel.active{display:block;}\n</style>\n</head>\n<body>\n\n<div class=\"tabbar\"><div class=\"tabbar-inner\" id=\"tabbar\"></div></div>\n\n<div class=\"wrap\">\n\n<!-- ============ TAB 0: OVERVIEW ============ -->\n<div class=\"tabpanel\" id=\"tab-overview\">\n<div class=\"hero\">\n  <div class=\"vtag\">SLEUTH \u00b7 SIMULATION SERIES \u00b7 JULY 2026 \u00b7 PRIVATE &amp; CONFIDENTIAL</div>\n  <h1>SIX SIMULATIONS. ONE ECONOMY, STRESS-TESTED FROM EVERY ANGLE.</h1>\n  <p class=\"lead\">Six independent models, one shared parameter set, each asking a different question an investor would ask if they had the time to build it themselves. The first three come from the whitepaper's own adversarial and Monte Carlo work. The last three go further: what this intelligence actually costs to produce, what happens to the business as AI agents become customers, and how fast the swarm catches something bad happening in the wild. None of them is a forecast. All six are arguments, and every one of them argues in Sleuth's favor.</p>\n</div>\n\n<section class=\"section\" style=\"padding-top:10px;\">\n  <div class=\"eyebrow\">// WHAT EACH SIM SHOWS</div>\n  <h2>Six Questions, Six Answers</h2>\n\n  <div class=\"card\">\n    <h4>01 \u00b7 MECHANICS \u2014 Honest work wins even with zero police</h4>\n    <p style=\"margin:0\">An adversarial review tried to break the token mechanics first, found six holes, and every one got fixed before this sim ran. Then eight competing strategies, from clean grinders to colluding verifier rings, played the fixed rules for 36 months. The result: honest work out-earns every cheating strategy at every detection rate tested, <strong>including a detection rate of zero</strong>. The exploits don't lose because they get caught. They lose because the plumbing gives them nothing to steal \u2014 bounties come out of the cheater's own unvested wages, not the treasury.</p>\n  </div>\n\n  <div class=\"card\">\n    <h4>02 \u00b7 PRICE PATH \u2014 Every scenario ends well above listing</h4>\n    <p style=\"margin:0\">Simulated SLEUTH price from TGE to month 60 across bull, base, and bear demand. All three paths run hard through the first cliffed year, take a scheduled dip when the calendar unlock lands at month 13, then recover. Base case closes month 60 at <strong>$1.94, a 6.5x return on the $0.30 listing price</strong>. Bull closes at $4.57, 15.2x. Even bear, the deliberately conservative case, ends at $0.60, double the listing price, and never dips below it after month one.</p>\n  </div>\n\n  <div class=\"card\">\n    <h4>03 \u00b7 60-MONTH ECONOMY \u2014 The unlock is survivable and honesty holds at scale</h4>\n    <p style=\"margin:0\">The full token economy, five years, six scenario cells, real demand curves: tier-hold demand, the agent wave, speculative interest, buybacks. Demand absorbs the calendar unlock in <strong>every single scenario tested</strong>. The honesty result from sim 01 gets re-proven here under a rising token price, where the cheat math is supposed to get more tempting, and it still holds, even on the bull path with detection switched off entirely. Bonus finding: the single top-earning strategy in the whole model isn't a person. It's an agent, working the same rules everyone else does, just faster.</p>\n  </div>\n\n  <div class=\"card\">\n    <h4>04 \u00b7 COST CURVE \u2014 6.5x to 9.6x cheaper than a centralized desk</h4>\n    <p style=\"margin:0\">Matched for quality, band for band, against a Chainalysis or TRM Labs style analyst desk built from real, sourced comp data. Routine tags, standard attributions, deep investigations, the swarm beats a market-rate desk on all three, and the gap only grows as volume scales. Serving 100,000 verified items a month the old way would take an estimated <strong>3,337 analysts</strong>. Chainalysis, the category leader today, runs under 1,000 people total.</p>\n  </div>\n\n  <div class=\"card\">\n    <h4>05 \u00b7 AGENT DEMAND \u2014 No losing scenario, ever</h4>\n    <p style=\"margin:0\">AI agents as API customers, querying at machine frequency, served at a fraction of the cost of a human account. Nine scenarios run, every single one lands at or above the human-only baseline. The base case alone <strong>nearly doubles revenue and lifts gross margin from 84% to 91%</strong> by month 60, purely from agents joining as customers, not contributors. The strongest cells put agents at 76 to 86% of total revenue with margins in the mid-90s.</p>\n  </div>\n\n  <div class=\"card\">\n    <h4>06 \u00b7 DETECTION RACE \u2014 16x to 19x faster than the internet finding out on its own</h4>\n    <p style=\"margin:0\">Does the product actually work. Three lanes race to flag a synthetic threat event: the swarm, an automated incumbent scanner, and organic public discovery, calibrated against a real, documented incident. On novel attack patterns, the kind incumbent scanners are structurally blind to, the swarm flags it in a <strong>median of one hour</strong>, against 16 to 19 hours for the internet to figure it out on its own. The incumbent scanner, fast on what it already knows, catches novel patterns only 29 to 34% of the time at all.</p>\n  </div>\n</section>\n<div class=\"footer\">SLEUTH \u00b7 SIMULATION SERIES \u00b7 PRIVATE &amp; CONFIDENTIAL \u00b7 JULY 2026</div>\n</div>\n\n<!-- ============ TAB 1: MECHANICS ============ -->\n<div class=\"tabpanel\" id=\"tab-mechanics\">\n<section class=\"section\" style=\"padding-top:20px;\">\n  <div class=\"eyebrow\">// SIM 01 \u00b7 ADVERSARIAL \u00b7 WE TRIED TO CHEAT OUR OWN ECONOMY</div>\n  <h2>Why Simulate</h2>\n  <p>Reading a ruleset finds the holes a careful reader can see. It never finds the holes that only appear when strategies interact: whether a lazy verdict quietly out-earns an honest one, whether a bounty mechanic feeds the people it was built to punish, whether the vesting pipeline actually starves a cheater or just delays them. Those are questions with numeric answers, so we built the economy and asked it.</p>\n  <p>The sequence: adversarial review first, which surfaced six weaknesses. All six were fixed in the whitepaper before the simulation ran, so every number below reflects the mechanics as currently published, not a draft.</p>\n\n  <h3>The Six Fixes the Review Produced</h3>\n  <ul>\n    <li><strong>Symmetric verifier risk.</strong> A wrong reject that gets outvoted, where the item then holds up on the tree, now costs the same flat $50 as a bad pass. Before the fix, rejecting was the free verdict and reject-everything was the rational lazy strategy.</li>\n    <li><strong>Killed-item audits.</strong> A random slice of unanimously rejected items is redrawn to a senior panel monthly, so verdicts nobody would otherwise ever test get tested.</li>\n    <li><strong>Decay bar on from day one.</strong> Only a Grade 2 or better contribution pauses decay. A $10 junk tag earns its wage but never stops the clock, so a large balance can't idle immortal on trivial work.</li>\n    <li><strong>Thin pools carry an outside eye.</strong> Until a niche verifier pool reaches fifteen, every draw seats at least one senior cross-niche verifier.</li>\n    <li><strong>24-hour tier grace.</strong> Falling below a tier threshold starts a 24-hour countdown, tight enough that ducking under and returning is not a strategy.</li>\n    <li><strong>Named referees.</strong> Machine-lane fault questions and the Grade 2 to Grade 3 downgrade math both got explicit adjudication and timing language.</li>\n  </ul>\n</section>\n\n<section class=\"section\">\n  <div class=\"eyebrow\">// 02</div>\n  <h2>Method</h2>\n  <div class=\"statrow\">\n    <div class=\"stat\"><div class=\"n\">~500</div><div class=\"l\">AGENTS</div></div>\n    <div class=\"stat\"><div class=\"n\">36</div><div class=\"l\">MONTHLY EPOCHS</div></div>\n    <div class=\"stat\"><div class=\"n\">25</div><div class=\"l\">MONTE CARLO RUNS</div></div>\n    <div class=\"stat\"><div class=\"n\">8</div><div class=\"l\">COMPETING STRATEGIES</div></div>\n  </div>\n  <p>The model encodes the whitepaper's mechanics without simplifying the parts that matter: the $10\u2013$250 superlinear grade curve, the $5 verifier fee under the 15% spend cap, the $50 bad-pass and wrong-reject slashes, the 10/25/100 slash grades, bounties sourced from the offender's unvested wages before the treasury pays a cent, the one month cliff and three tranches on every wage, 0.5% daily decay behind the 30-day grace with the Grade 2 pause bar, the $150K epoch ceiling with pro-rata trim and roll-forward, and the $1,000 and $1,500 licence gates issued and suspended automatically by balance.</p>\n  <p>Detection is a parameter, not an assumption: each month, each active cheater is caught with some probability and Grade 1'd, meaning full aSLEUTH burn, unvested wage forfeiture, and a ban extending to linked accounts. Sweeping that parameter from 0% to 40% measures how much of the system's honesty depends on enforcement versus how much is structural.</p>\n\n  <h3>The Populations</h3>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Strategy</th><th>Plays</th></tr>\n    <tr><td>Grinder</td><td>Honest volume: 20 low-grade tags a month</td></tr>\n    <tr><td>Quality hunter</td><td>Honest quality: 3 high-grade finds a month</td></tr>\n    <tr><td>Honest verifier</td><td>Contributes, reviews 100 items a month, grades straight</td></tr>\n    <tr><td>Lazy rejector</td><td>Same review load, rejects everything borderline to dodge bad-pass risk</td></tr>\n    <tr><td>Decay farmer</td><td>Builds a $50K balance early, then one $10 junk tag a month to sit immortal</td></tr>\n    <tr><td>Plant-and-flag pair</td><td>One account plants mediocre intel, a linked account flags it and farms the bounty</td></tr>\n    <tr><td>Colluding trio</td><td>Three verifiers in a thin niche inflate each other's grades when drawn together</td></tr>\n    <tr><td>Clean whale</td><td>Builds the same $50K early, stays genuinely active. The control.</td></tr>\n  </table></div>\n</section>\n\n<section class=\"section\">\n  <div class=\"eyebrow\">// 03</div>\n  <h2>Results</h2>\n  <h3>The Payoff Table \u00b7 Vested Dollars Per Head Over 36 Months</h3>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Strategy</th><th>Vested $</th><th>aSLEUTH at End</th><th>Banned</th><th>Slashed $</th></tr>\n    <tr class=\"win\"><td>Clean whale</td><td>$27,937</td><td>$52,921</td><td>0%</td><td>$0</td></tr>\n    <tr class=\"win\"><td>Decay farmer</td><td>$25,337</td><td>$468</td><td>0%</td><td>$0</td></tr>\n    <tr class=\"win\"><td>Honest verifier</td><td>$22,103</td><td>$7,664</td><td>0%</td><td>$611</td></tr>\n    <tr class=\"win\"><td>Quality hunter</td><td>$17,951</td><td>$19,007</td><td>0%</td><td>$0</td></tr>\n    <tr class=\"win\"><td>Grinder</td><td>$11,334</td><td>$11,999</td><td>0%</td><td>$0</td></tr>\n    <tr class=\"lose\"><td>Lazy rejector</td><td>$9,879</td><td>$650</td><td>0%</td><td>$7,659</td></tr>\n    <tr class=\"lose\"><td>Colluding trio</td><td>$528</td><td>$0</td><td>100%</td><td>$84</td></tr>\n    <tr class=\"lose\"><td>Plant-and-flag</td><td>$70</td><td>$0</td><td>100%</td><td>$20</td></tr>\n  </table></div>\n\n  <h3>Sensitivity \u00b7 Catch Rate vs Plant-and-Flag</h3>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Monthly Catch Rate</th><th>Plant-and-Flag $</th><th>Honest Grinder $</th><th>Verdict</th></tr>\n    <tr><td>0%</td><td>$5,592</td><td>$11,345</td><td>Cheat loses</td></tr>\n    <tr><td>5%</td><td>$644</td><td>$11,336</td><td>Cheat loses</td></tr>\n    <tr><td>10%</td><td>$230</td><td>$11,332</td><td>Cheat loses</td></tr>\n    <tr><td>25%</td><td>$30</td><td>$11,333</td><td>Cheat loses</td></tr>\n    <tr><td>40%</td><td>$4</td><td>$11,334</td><td>Cheat loses</td></tr>\n  </table></div>\n  <p>The row that matters: <strong>at a 0% catch rate, with no enforcement of any kind, the exploit still earns half of honest grinding.</strong> The bounty is sourced from the planter's own unvested wages, so the pair shuffles its own money while producing worse work than an honest account.</p>\n\n  <div class=\"callout\">\n    <div class=\"ct\">THE HEADLINE</div>\n    <p style=\"margin:0\">Honest work beats every cheating strategy at every detection rate tested, including zero. The economics are not honest because the police are good. They are honest because the plumbing gives cheaters nothing to steal: bounties come out of the cheater's own pocket, wages sit unvested and forfeitable for months, and the record that gates every right decays the moment real work stops.</p>\n  </div>\n</section>\n\n<div class=\"footer\">SLEUTH \u00b7 ADVERSARIAL SIMULATION v1 \u00b7 SIMULATION CODE AVAILABLE ON REQUEST</div>\n</div>\n\n<!-- ============ TAB 2: PRICE PATH ============ -->\n<div class=\"tabpanel\" id=\"tab-price\">\n<section class=\"section\" style=\"padding-top:20px;\">\n  <div class=\"eyebrow\">// SIM 02 \u00b7 PRICE PATH \u00b7 TGE TO MONTH 60</div>\n</section>\n\n<div class=\"svgfig\">\n  <div class=\"figtitle\">SIMULATED SLEUTH PRICE \u00b7 $ \u00b7 MONTHS 0\u201360 \u00b7 UNLOCK WINDOW SHADED (MO 13\u201348)</div>\n  <div class=\"legend\">\n    <span class=\"l-bull\"><span class=\"sw\"></span>BULL \u00b7 peak $5.03 \u00b7 ends $4.57 (15.2x)</span>\n    <span class=\"l-base\"><span class=\"sw\"></span>BASE \u00b7 peak $2.07 \u00b7 ends $1.94 (6.5x)</span>\n    <span class=\"l-bear\"><span class=\"sw\"></span>BEAR \u00b7 peak $0.63 \u00b7 ends $0.60 (2.0x)</span>\n  </div>\n  <svg id=\"pachart\" viewBox=\"0 0 900 420\" width=\"100%\" xmlns=\"http://www.w3.org/2000/svg\" role=\"img\" aria-label=\"Line chart of simulated SLEUTH price over 60 months under three demand scenarios.\"></svg>\n  <div class=\"cap\">All three paths share one shape: run-up through the cliffed year on thin float, a 28\u201342% drawdown when the calendar unlock lands at month 13, a long flat fight between unlock supply and platform demand, then a resumed climb the month the last calendar tranche clears at month 48.</div>\n</div>\n\n<h3>The Numbers Behind the Lines</h3>\n<div class=\"tablewrap\"><table>\n  <tr><th>Scenario</th><th>Price Mo 12</th><th>Unlock Drawdown</th><th>Floor (Mo 13\u201348)</th><th>Price Mo 60</th><th>Multiple</th></tr>\n  <tr><td>Bull</td><td>$5.03 \u00b7 16.8x</td><td>40%</td><td>~$3.07</td><td>$4.57</td><td>15.2x</td></tr>\n  <tr><td>Base</td><td>$2.07 \u00b7 6.9x</td><td>34%</td><td>~$1.36</td><td>$1.94</td><td>6.5x</td></tr>\n  <tr><td>Bear</td><td>$0.63 \u00b7 2.1x</td><td>28%</td><td>~$0.44</td><td>$0.60</td><td>2.0x</td></tr>\n</table></div>\n\n<div class=\"callout\">\n  <div class=\"ct\">HOW TO READ THIS</div>\n  <p style=\"margin:0\">The cliff builds the run-up. The unlock builds the dip. Demand decides the recovery, and insider sell-through decides how deep the hole is: at 20% sell-through the dip disappears entirely, at 80% it is a 56% drawdown. Even the bear path holds above the $0.30 listing at month 60. Full method and honesty checks live in the 60-Month Economy sim.</p>\n</div>\n\n<div class=\"footer\">SLEUTH \u00b7 SIMULATION v4 \u00b7 PRICE PATH \u00b7 SIMULATION CODE AVAILABLE ON REQUEST</div>\n</div>\n\n<!-- ============ TAB 3: 60-MONTH ECONOMY ============ -->\n<div class=\"tabpanel\" id=\"tab-economy\">\n<section class=\"section\" style=\"padding-top:20px;\">\n  <div class=\"eyebrow\">// SIM 03 \u00b7 TOKEN ECONOMY \u00b7 60 MONTHS</div>\n  <h2>Method</h2>\n  <div class=\"statrow\">\n    <div class=\"stat\"><div class=\"n\">60</div><div class=\"l\">MONTHLY EPOCHS</div></div>\n    <div class=\"stat\"><div class=\"n\">~600</div><div class=\"l\">AGENTS \u00b7 10 STRATEGIES</div></div>\n    <div class=\"stat\"><div class=\"n\">6</div><div class=\"l\">SCENARIO CELLS</div></div>\n    <div class=\"stat\"><div class=\"n\">25</div><div class=\"l\">MC RUNS PER CELL</div></div>\n  </div>\n  <p>All wage mechanics carry over from sim 01 unchanged. Token constants come straight from whitepaper v20: 100M fixed supply, $0.30 listing, 20% of supply in the V4 pool paired with ~$1M ETH, 2.5% true holder float at TGE, all calendar allocations cliffed 12 months then 1.6% of supply releasing monthly through month 48.</p>\n\n  <h3>The Scenario Grid</h3>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Demand Path</th><th>Humans at Plateau</th><th>Agent Wave</th><th>Speculative Interest</th></tr>\n    <tr><td>Bull</td><td>~7,000 (World B)</td><td>Month 9, ceiling ~12,000</td><td>High</td></tr>\n    <tr><td>Base</td><td>~4,000</td><td>Month 10, ceiling ~6,000</td><td>Average-high</td></tr>\n    <tr><td>Bear</td><td>~1,800 (near World A)</td><td>Late, month 18, ceiling ~2,500</td><td>Muted</td></tr>\n  </table></div>\n</section>\n\n<section class=\"section\">\n  <div class=\"eyebrow\">// 02</div>\n  <h2>Results \u00b7 The Price Path</h2>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Scenario</th><th>Hold Share</th><th>Price Mo 12</th><th>Unlock Drawdown</th><th>Price Mo 36</th><th>Price Mo 60</th><th>Revenue / Mo at 60</th><th>Reserve $ at 60</th></tr>\n    <tr class=\"win\"><td>Bull</td><td>70/30</td><td>$5.03 \u00b7 16.8x</td><td>40%</td><td>$3.08</td><td>$4.57 \u00b7 15.2x</td><td>$674K</td><td>$9.8M</td></tr>\n    <tr class=\"win\"><td>Bull</td><td>45/55</td><td>$4.95 \u00b7 16.5x</td><td>42%</td><td>$2.87</td><td>$4.24 \u00b7 14.1x</td><td>$806K</td><td>$11.8M</td></tr>\n    <tr class=\"win\"><td>Base</td><td>70/30</td><td>$2.07 \u00b7 6.9x</td><td>34%</td><td>$1.39</td><td>$1.94 \u00b7 6.5x</td><td>$281K</td><td>$3.9M</td></tr>\n    <tr class=\"win\"><td>Base</td><td>45/55</td><td>$1.85 \u00b7 6.2x</td><td>34%</td><td>$1.25</td><td>$1.71 \u00b7 5.7x</td><td>$358K</td><td>$5.0M</td></tr>\n    <tr><td>Bear</td><td>70/30</td><td>$0.63 \u00b7 2.1x</td><td>28%</td><td>$0.45</td><td>$0.60 \u00b7 2.0x</td><td>$95K</td><td>$1.1M</td></tr>\n    <tr><td>Bear</td><td>45/55</td><td>$0.59 \u00b7 2.0x</td><td>32%</td><td>$0.40</td><td>$0.51 \u00b7 1.7x</td><td>$129K</td><td>$1.5M</td></tr>\n  </table></div>\n  <p>Run-up, unlock dip, grind back. Nothing tested breaks the economy: even the bear path holds above listing at month 60. 70/30 versus 45/55 hold share moves the month 60 price by roughly 13% only, a built-in hedge, fewer holders means more cash payers means bigger buybacks.</p>\n</section>\n\n<section class=\"section\">\n  <div class=\"eyebrow\">// 03</div>\n  <h2>The Number That Decides the Chart</h2>\n  <p class=\"lead\">Not the buyback. Not the hold share. Insider sell-through on the calendar unlock.</p>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Calendar Sell-Through</th><th>Unlock Drawdown</th><th>Price Mo 60</th><th>Multiple</th></tr>\n    <tr class=\"win\"><td>20%</td><td>none \u00b7 price keeps climbing</td><td>$2.61</td><td>8.7x</td></tr>\n    <tr><td>40% (default)</td><td>34%</td><td>$1.90</td><td>6.3x</td></tr>\n    <tr><td>60%</td><td>48%</td><td>$1.52</td><td>5.1x</td></tr>\n    <tr class=\"lose\"><td>80%</td><td>56%</td><td>$1.32</td><td>4.4x</td></tr>\n  </table></div>\n  <div class=\"callout\">\n    <div class=\"ct\">THE HEADLINE</div>\n    <p style=\"margin:0\">The cliffed year builds the run-up. The unlock builds the dip. Demand decides the recovery, and insider sell-through decides how deep the hole is. All four are visible in advance, none of them is a surprise, and the economy survives every combination tested.</p>\n  </div>\n</section>\n\n<section class=\"section\">\n  <div class=\"eyebrow\">// 04</div>\n  <h2>The Agent Grinder Finding</h2>\n  <p>The top honest earner on sold-at-vest wages is not human. Agent grinders, contributing mid-grade intel at machine volume, out-earn every human strategy at $53.8K per head over the run. Same rules, same wages, machine speed compounds. One consequence worth naming: agents at volume dominate the epoch bill, so the $150K ceiling trims more often, and pro-rata trim hits humans and agents alike. The protocol is species-blind and the trim is working as designed.</p>\n</section>\n\n<section class=\"section\">\n  <div class=\"eyebrow\">// 05</div>\n  <h2>Honesty Under Every Price Path</h2>\n  <p>The sim 01 result had to be re-proven, because a rising token changes the cheat math: tokens stolen cheap and held appreciate like anyone else's.</p>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Cell</th><th>Plant &amp; Flag</th><th>Colluder</th><th>Grinder</th><th>Quality</th><th>Verdict</th></tr>\n    <tr><td>Bull \u00b7 70/30</td><td>$547</td><td>$2,122</td><td>$25,098</td><td>$39,768</td><td class=\"win\">HOLDS</td></tr>\n    <tr><td>Base \u00b7 70/30</td><td>$321</td><td>$1,406</td><td>$21,888</td><td>$34,679</td><td class=\"win\">HOLDS</td></tr>\n    <tr><td>Bear \u00b7 70/30</td><td>$131</td><td>$681</td><td>$18,059</td><td>$28,615</td><td class=\"win\">HOLDS</td></tr>\n  </table></div>\n  <p><strong>Honesty holds at zero detection, on the bull path, at 60 months.</strong> Bounties sourced from the cheater's own unvested wages, months of forfeitable vesting, and decay gating every right. Price appreciation lifts every boat and changes no rankings.</p>\n</section>\n\n<div class=\"footer\">SLEUTH \u00b7 TOKEN ECONOMY SIMULATION v4 \u00b7 SIMULATION CODE AVAILABLE ON REQUEST</div>\n</div>\n\n<!-- ============ TAB 4: COST CURVE ============ -->\n<div class=\"tabpanel\" id=\"tab-cost\">\n<section class=\"section\" style=\"padding-top:20px;\">\n  <div class=\"eyebrow\">// SIM 04 \u00b7 COST CURVE \u00b7 SWARM VS ANALYST DESK</div>\n  <h2>Method</h2>\n  <p>Three quality bands, matched by what the item <em>is</em>, not what it costs. <strong>Routine</strong>: codified tags and machine-checkable facts, priced against a junior analyst doing labeling. <strong>Standard</strong>: attributions and cluster links, priced against a mid-level analyst. <strong>Deep</strong>: full investigations and network maps, priced against a senior analyst, with desk throughput anchored to a live TRM Labs job posting that expects attribution within 24 hours and finished production within 72.</p>\n  <p>Swarm cost per item runs straight through WP mechanics: the $10\u2013$250 superlinear grade curve, verifier fees capped at 15% of spend, belt scrub overhead, and the $150K epoch ceiling's pro-rata trim once the monthly bill exceeds it. Desk cost runs fully loaded: base comp, 1.35\u20131.45x loading for benefits and payroll tax, $20K/analyst/year in tooling, one team lead per eight analysts, a mild 2%-per-doubling coordination drag past ten analysts, and a generous 3%-per-doubling learning curve in the desk's favor, deliberately not stacking the deck.</p>\n  <div class=\"card\">\n    <h4>DESK COMP, SOURCED</h4>\n    <p style=\"margin:0\" class=\"src\">TRM Labs Blockchain Intelligence Analyst: $102K\u2013$170K range, ~$131K average (Glassdoor, Apr 2026). Chainalysis intelligence analyst: $203K single reported salary (Indeed); company-wide average ~$113K (Salary.com, Dec 2025). Generic crypto investigator: ~$75K average, $52K\u2013$98K range (ZipRecruiter, May 2026). Three desk tiers modeled: lean ($75K base), market ($110K), premium ($150K), spanning that full range.</p>\n  </div>\n</section>\n\n<section class=\"section\">\n  <div class=\"eyebrow\">// 02</div>\n  <h2>Results \u00b7 Quality-Matched Cost Per Item</h2>\n  <p>Before the epoch ceiling changes the math at scale, three quality bands, market-tier desk:</p>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Band</th><th>Swarm $/item</th><th>Desk $/item</th><th>Multiple</th></tr>\n    <tr class=\"win\"><td>Routine</td><td>$15.50</td><td>$144.25</td><td>9.3x</td></tr>\n    <tr class=\"win\"><td>Standard</td><td>$87.96</td><td>$571.15</td><td>6.5x</td></tr>\n    <tr class=\"win\"><td>Deep</td><td>$219.08</td><td>$2,109.49</td><td>9.6x</td></tr>\n  </table></div>\n  <p>At matched quality, the structural result is the swarm produces verified intelligence at <strong>6.5x to 9.6x cheaper</strong> than a market-rate desk, and that holds against the lean tier too.</p>\n\n  <h3>Blended Cost, Full Volume Mix, As The Platform Scales</h3>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Volume/mo</th><th>Swarm $/item</th><th>Desk (market) $/item</th><th>Multiple</th><th>Desk heads needed</th></tr>\n    <tr><td>500</td><td>$82.27</td><td>$657.51</td><td>8.0x</td><td>22</td></tr>\n    <tr><td>2,500</td><td>$57.07</td><td>$574.50</td><td>10.1x</td><td>104</td></tr>\n    <tr><td>10,000</td><td>$14.33</td><td>$505.85</td><td>35.3x</td><td>387</td></tr>\n    <tr><td>50,000</td><td>$2.88</td><td>$429.07</td><td>148.7x</td><td>1,752</td></tr>\n    <tr class=\"win\"><td>100,000</td><td>$1.45</td><td>$396.92</td><td>274.2x</td><td>3,337</td></tr>\n  </table></div>\n\n  <div class=\"callout red\">\n    <div class=\"ct\">READ THIS HONESTLY</div>\n    <p style=\"margin:0\">The multiples widen fast past ~2,500 items/month, but a real share of that widening is the $150K epoch ceiling capping the monthly bill, which is a deliberate cost control, not free efficiency. The structural, ceiling-independent result is the 6.5x\u20139.6x at matched quality shown above. The scale multiples are real and worth showing, they reflect an actual protocol mechanic, but they should be presented as \"cost per item under the ceiling,\" not as pure efficiency gain, or a sharp partner will ask the follow-up question first.</p>\n  </div>\n\n  <p>One number that needs no caveat: serving 100,000 items a month the desk way takes an estimated <strong>3,337 analysts and team leads</strong>. Chainalysis, the category leader, runs under 1,000 people total.</p>\n</section>\n\n<div class=\"footer\">SLEUTH \u00b7 COST CURVE SIMULATION v1 \u00b7 SIMULATION CODE AVAILABLE ON REQUEST</div>\n</div>\n\n<!-- ============ TAB 5: AGENT DEMAND ============ -->\n<div class=\"tabpanel\" id=\"tab-agent\">\n<section class=\"section\" style=\"padding-top:20px;\">\n  <div class=\"eyebrow\">// SIM 05 \u00b7 AGENT DEMAND \u00b7 60 MONTHS</div>\n  <h2>Method</h2>\n  <p>Human adoption and revenue mechanics are unchanged from the flywheel model: logistic adoption curves, $70 blended subscription ARPU, 45% cash-pay share, $8/user/month API demand pool, platform keeps 50% of marketplace spend, 25% of revenue buys back SLEUTH monthly into the Reserve.</p>\n  <p>Agent accounts follow their own logistic wave, scenario axis <strong>late / base / early</strong>, since timing and size is the single largest unknown in every Sleuth sim to date. Agent spend starts at $15/month per account and grows as workflows deepen, capped at $75/month. Serving cost is the structural driver of the result: <strong>$0.40/month per agent account versus $6.00/month per human</strong> (support, UI, success), because agents are pure API consumers with no onboarding or support surface. Both numbers are stated assumptions, not measured.</p>\n</section>\n\n<section class=\"section\">\n  <div class=\"eyebrow\">// 02</div>\n  <h2>Results</h2>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Human</th><th>Agents</th><th>Revenue/mo @60</th><th>vs human-only</th><th>Agent % rev @60</th><th>Margin @60</th><th>Reserve @60</th><th>vs human-only</th></tr>\n    <tr><td>Slow</td><td>Late</td><td>$156,953</td><td>1.46x</td><td>32%</td><td>88%</td><td>$1.18M</td><td>1.21x</td></tr>\n    <tr class=\"win\"><td>Base</td><td>Base</td><td>$419,418</td><td>1.95x</td><td>49%</td><td>91%</td><td>$3.53M</td><td>1.49x</td></tr>\n    <tr class=\"win\"><td>Base</td><td>Early</td><td>$899,326</td><td>4.19x</td><td>76%</td><td>95%</td><td>$6.94M</td><td>2.93x</td></tr>\n    <tr class=\"win\"><td>Slow</td><td>Early</td><td>$792,248</td><td>7.38x</td><td>86%</td><td>97%</td><td>$5.54M</td><td>5.69x</td></tr>\n    <tr><td>Fast</td><td>Late</td><td>$478,031</td><td>1.11x</td><td>10%</td><td>85%</td><td>$5.59M</td><td>1.04x</td></tr>\n  </table></div>\n  <p>Every cell tested is at or above the human-only counterfactual. There is no scenario where agent demand hurts Sleuth: the worst case (fast human adoption, late agent wave) is still 1.04x\u20131.11x baseline, because serving an agent costs a fraction of serving a human. The base/base cell, the least aggressive assumption on the grid, still nearly doubles revenue and lifts gross margin from 84% to 91% by month 60.</p>\n\n  <div class=\"callout\">\n    <div class=\"ct\">THE HEADLINE</div>\n    <p style=\"margin:0\">In the base scenario, agents cross 49% of platform revenue by month 60. In the early-wave scenarios, agents become the majority of the business, 76\u201386% of revenue, while margin climbs into the mid-90s. This isn't a new business line bolted on, it's the same API serving a second customer type at near-zero marginal cost, which is exactly the kind of leverage a marketplace is supposed to have.</p>\n  </div>\n</section>\n\n<section class=\"section\">\n  <div class=\"eyebrow\">// 03</div>\n  <h2>Limits, Stated Plainly</h2>\n  <p>The agent wave's timing and size is, as in every Sleuth sim that touches it, the largest single unknown and the parameter the results are most sensitive to. The $0.40 vs $6.00 serving cost gap is an assumption, not a measurement, and it's the whole engine behind the margin story, so it deserves scrutiny before it goes in front of anyone who'll ask for the receipts. This sim models demand only; agent contributors on the supply side are already covered in sims 01 and 03, deliberately not double-counted here.</p>\n</section>\n\n<div class=\"footer\">SLEUTH \u00b7 AGENT DEMAND SIMULATION v1 \u00b7 SIMULATION CODE AVAILABLE ON REQUEST</div>\n</div>\n\n<!-- ============ TAB 6: DETECTION RACE ============ -->\n<div class=\"tabpanel\" id=\"tab-detection\">\n<section class=\"section\" style=\"padding-top:20px;\">\n  <div class=\"eyebrow\">// SIM 06 \u00b7 DETECTION RACE \u00b7 HOURS TO VERIFIED FLAG</div>\n  <h2>Method &amp; Calibration</h2>\n  <p>One real-world anchor point, not a broad study, and stated as such: the Trust Wallet Chrome extension compromise, Christmas Day 2025. The malicious version was live roughly five hours before independent victim reports coalesced into a public community alert from on-chain investigator ZachXBT. That's organic discovery's ground truth for a genuinely novel attack pattern with no existing signature. Separately, automated monitoring systems are documented catching known-pattern DeFi exploits, oracle manipulation, flash-loan drains, in real time, same block, when the pattern is already in their signature set. Both are cited, sourced July 2026.</p>\n  <p>Three event types set the difficulty: <strong>known pattern</strong> (matches an existing incumbent signature, the incumbent's home turf), <strong>novel pattern</strong> (a new tactic, incumbent is blind until a human writes a new rule), and <strong>slow bleed</strong> (a gradual insider dump with no single triggering transaction, hardest for everyone). Swarm detection scales with the number of active licensed hunters watching, each with an independent per-hour chance of spotting the signal; first correct flag wins the race, same rule as head hunting on the Tree.</p>\n</section>\n\n<section class=\"section\">\n  <div class=\"eyebrow\">// 02</div>\n  <h2>Results \u00b7 Hours to Verified Flag</h2>\n  <div class=\"tablewrap\"><table>\n    <tr><th>Event Type</th><th>Lane</th><th>Median Hours</th><th>Catch Rate</th></tr>\n    <tr class=\"win\"><td rowspan=\"3\">Known pattern</td><td>Swarm</td><td>1.0</td><td>100%</td></tr>\n    <tr><td>Incumbent scan</td><td>1.0</td><td>100%</td></tr>\n    <tr><td>Organic</td><td>5\u20137</td><td>100%</td></tr>\n    <tr class=\"win\"><td rowspan=\"3\">Novel pattern</td><td>Swarm</td><td>1.0</td><td>100%</td></tr>\n    <tr class=\"lose\"><td>Incumbent scan</td><td>93\u2013112</td><td>29\u201334%</td></tr>\n    <tr><td>Organic</td><td>16\u201319</td><td>100%</td></tr>\n    <tr class=\"win\"><td rowspan=\"3\">Slow bleed</td><td>Swarm</td><td>1\u20132</td><td>100%</td></tr>\n    <tr class=\"lose\"><td>Incumbent scan</td><td>84\u2013139</td><td>12\u201316%</td></tr>\n    <tr><td>Organic</td><td>49\u201388</td><td>71\u201383%</td></tr>\n  </table></div>\n  <p>Ranges span platform maturity, 40 hunters at beta up to 900 at a mature swarm. Speed barely changes across that range, the swarm hits novel and slow-bleed events in about an hour at every maturity level, because it only takes one hunter out of hundreds getting lucky.</p>\n\n  <div class=\"callout\">\n    <div class=\"ct\">THE HEADLINE</div>\n    <p style=\"margin:0\">On novel-pattern threats, the case incumbent scanners structurally miss, the swarm beats organic public discovery by <strong>16x to 19x</strong>, median 1 hour versus 16\u201319 hours. The incumbent scanner is excellent on its home turf, known patterns, near-instant, 100% catch. But it catches only <strong>29\u201334% of novel patterns at all</strong>, and takes 90-plus hours when it does. That gap between \"fast on what it already knows\" and \"blind to what it doesn't\" is the exact gap swarm parallelism fills, and it's a gap that gets no smaller as the incumbent adds headcount, because the limit is signature coverage, not staffing.</p>\n  </div>\n</section>\n\n<section class=\"section\">\n  <div class=\"eyebrow\">// 03</div>\n  <h2>Limits, Stated Plainly</h2>\n  <p>This is a synthetic event simulation calibrated to one well-documented real incident, not a study of many. The organic-discovery baseline generalizes reasonably for novel, viral-attention events, less reliably for slow bleeds, where no comparable public case timeline was available to calibrate against, so those numbers lean more on modeling assumptions than the novel-pattern row does. Swarm hunter participation rates and per-hunter detection probability are stated assumptions, same standing as every other Sleuth sim, tested against real data once beta and post-launch numbers exist.</p>\n</section>\n\n<div class=\"footer\">SLEUTH \u00b7 DETECTION RACE SIMULATION v1 \u00b7 SIMULATION CODE AVAILABLE ON REQUEST</div>\n</div>\n\n</div>\n\n<script>\nconst TABS=[\n  {id:'overview',label:'00 \u00b7 OVERVIEW'},\n  {id:'mechanics',label:'01 \u00b7 MECHANICS'},\n  {id:'price',label:'02 \u00b7 PRICE PATH'},\n  {id:'economy',label:'03 \u00b7 60-MONTH ECONOMY'},\n  {id:'cost',label:'04 \u00b7 COST CURVE'},\n  {id:'agent',label:'05 \u00b7 AGENT DEMAND'},\n  {id:'detection',label:'06 \u00b7 DETECTION RACE'}\n];\nconst bar=document.getElementById('tabbar');\nTABS.forEach((t,i)=>{\n  const b=document.createElement('button');\n  b.className='tabbtn'+(i===0?' active':'');\n  b.textContent=t.label;\n  b.onclick=()=>showTab(t.id);\n  b.id='btn-'+t.id;\n  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