Top Tweets for #ItsAI
@yxdxnx @kayamoonx @urasianannie @BigD_204 @kayamoonn @grok Here’s another AI rendering of “Kaya/Annie” cosplaying as Psylocke of the X-Men. AI made courtesy of Grok.
#OnlyFans #OnlyFansSecrets @kayamoonx #NotReal #ItsAI #AIArt
A AI rendering of @kayamoonx @yxdxnx @urasianannie cosplaying as Eve from Stellar Blade. Doesn’t she some what look like her?
#StellarBlade #StellarBladeNintendoSwitch2 @ShiftUpWorld @StellarBlade
#OnlyFans
@yxdxnx @kayamoonx @urasianannie @BigD_204 @kayamoonn @grok Another AI renactment of “Annie/Kaya” 😉 enjoy.
#OnlyFans #OnlyFansSecrets #NotReal #ItsAI #AIArt #OnlyAI #Fake #Reenactment
@leclercs16 This happening after AI becomes kinda powerful, I don’t think it’s a coincidence #notmygoat #itsAI #denial
In the age of infinite AI slop, @ai_detection SN32 is the truth filter the internet deserves.🔥
#Bittensor #SN32 #ItsAI $TAO
@ChaosOMwhen Does that mean mahoraga gets killed? The wheel is technically an AI that uses the data from the fight to come up with adaptions...
With the car benefits gone… walk-on just became literal. #ParkingLotLooksDifferent #NewSpotsAvailable #CheetahCountry #ItsAi @doug_kidd77810 @scotiepeacock @notcoachbrody
Drumroll please! The cougar known as the Fruitville Swindler saw his shadow this morning. That means six more weeks of cheating. #CheetahCountry #ItsAi @scotiepeacock @notcoachbrody @doug_kidd77810
Evidence? What evidence? I’m making confetti for the parade! #BeachGate #CheetahCountry #ItsAi #RobinsOnTheInside @scotiepeacock @doug_kidd77810 @notcoachbrody
OJ Simpson taught America an important lesson: confidence is saying ‘If the glove don’t fit’ and ignoring literally everything else. #ItsAi #Beachgate @notcoachbrody @scotiepeacock @doug_kidd77810 @TrueFruitville
Fortnite Chapter 7 Season 1: Pacific Break key art.
$TAO #Bittensor #DecentralizedAI #ItsAI
@ai_detection It's AI (Bittensor Subnet 32) — An investment brief
Scope: team, product, mechanism, roadmap, market, token/market structure, unit‑economics path, KPIs, risks, and a clear investability view.
Date: 22 Nov 2025.
0) TLDR
It’s AI (SN32) is a Bittensor subnet that crowdsources and rewards the best models to detect AI‑generated text, then packages that capability as a web app, API, browser extension, and integrations (with pricing for B2C, enterprise, and education). Subnet miners build/serve detectors; validators score them against labeled corpora and adversarial variants; alpha incentives push accuracy and low false‑positive rates.
1) Where SN32 stands today (on‑chain + product)
Subnet status & liquidity. SN32 “ItsAI” is live on mainnet. Taostats shows alpha pricing, supply, emissions share, pool reserves, UIDs (256/256), holder count, and recent trades.
Commercial surface. Public web app with deep scan (sentence/word‑level highlights), Chrome extension, ChatGPT plug‑in, Zapier, and API; B2C/Enterprise/Edu pricing published (Pro $20/mo, Enterprise from $95/mo, Edu $5/student/yr, plus PAYG packs).
Accuracy claims (external benchmarks). Their March‑2025 benchmarks report shows first‑place results on RAID (ACL 2024) and strong scores on GRiD/CUDRT; the site claims MGTD (ICAIE 2025) leadership and <1% FPR on a student dataset.
Team/public face. CEO Sergey Volnov is listed on the site; GitHub shows 500+ commits and multiple contributors; a Dubai entity (ITSAI TECHNOLOGIES FZCO) was formed in Jan‑2025 to support off‑chain commercialization.
Why this matters: SN32 is one of the few Bittensor subnets with:
(a) a working consumer‑facing product,
(b) transparent SaaS pricing,
and (c) a direct enterprise‑integration path—useful for post‑dTAO economics where real revenue increasingly matters.
2) What problem SN32 solves (and for whom)
Core problem: distinguishing human vs LLM‑generated text at scale with low false positives (FPR). The subnet’s stated philosophy: better to tolerate some AI usage than to wrongly penalize a human; metrics reflect that.
Users today: writers, marketers, teachers, editors (B2C); schools and universities (Edu); publishers/platforms/filters (B2B via API). The deck cites explicit market segments and competitors (GPTZero, CopyLeaks, ZeroGPT) with visible traction and revenue—evidence the category can monetize.
Illustrative narrative: think of an ad‑network spam filter or a university LMS that flags suspicious submissions. SN32’s models output a probability plus per‑sentence or per‑word contributions, making the verdict explainable for reviewers, not a black‑box ban hammer.
3) How the subnet works (mechanism & incentives)
3.1. Roles.
Miners host/serve detection models; validators query miners with a blend of human‑written and AI‑generated texts sourced from >30 open‑source LLMs, varied decoding parameters, plus text augmentations/adversarial attacks; validators compute performance metrics and submit weights to chain.
3.2. Scoring bias. Documentation emphasizes low FPR and uses multiple metrics (e.g., Average Precision over PR‑curves) to reward ranking quality; the final reward is the average of several metrics (AP plus two others defined in docs).
3.3. Emission dynamics. Under dTAO / flow‑based emissions, subnet rewards are influenced by stake flows/alpha market value, not just root validator judgments. This places a premium on external demand (API, subscriptions) that pulls TAO into SN32.
4) Product & roadmap (off‑chain commercialization)
4.1. Live features: deep scan, shareable reports, file uploads, batch scans, API, chrome extension; plagiarism checker, Moodle/Google Classroom integrations listed as “coming soon.”
4.2. Roadmap targets (deck): sequential growth from 15+ to 2,000+ premium users over four quarters (SEO, ads, funnel optimization; new channels and content integrations).
4.3. Go‑to‑market: start B2C for brand/volume, then expand enterprise (API/SLA) and education (LMS integrations). Market sizing slides cite ~$3.3B annual opportunity across English‑speaking B2C/B2B.
5) Competitive positioning
Category proves buyers will pay. GPTZero (venture‑backed), CopyLeaks (reported revenue), ZeroGPT (traffic scale). SN32’s pitch is higher measured accuracy on recent academic benchmarks plus explainability tooling and multilingual claims (e.g., Arabic).
Defensibility. The arms race is real—LLMs evolve to evade detection—but SN32’s incentive loop (validators constantly refreshing corpora/attacks) and multi‑metric scoring can adapt faster than a single closed model. The subnet’s open competition and frequent updates, if sustained, create a moving moat.
6) Business model & revenue path (can SN32 be profitable?)
Yes—plausibly—via diversified SaaS + API + EDU.
+ SaaS tiers create immediate B2C ARPU ($12–$20/mo) and PAYG packs; Enterprise subscriptions (from $95/mo) and API usage can scale with integrations; Edu per‑student pricing unlocks institutional budgets once LMS plugins ship.
+ Sales motion. Low‑friction B2C drives volume and brand; proof‑friendly deep scans reduce false claims risk—crucial for schools and employers. Enterprise wins likely start with email/content moderation and editorial integrity pilots.
+ Evidence of willingness to pay in the category (competitor revenue/valuations) plus SN32’s published pricing supports a path to positive gross margins (inference costs are modest vs. LLM generation).
7) Token/market structure (alpha) and positioning logic
Alpha price formation. Each subnet runs a constant‑product AMM against TAO; alpha price and emissions (via dTAO/flow) are functions of pool balances and stake flows. Expect slippage on large orders; use TWAP/DCA.
On‑chain health checks (daily). Track SN32’s emission share, pool depth (alpha/TAO), UIDs/miner churn, volume, and holders on Taostats before adjusting exposure.
Institutional stance: Constructive, with guardrails.
Treat SN32 alpha as an operating‑exposure to an app with real commercialization, sized by liquidity and validated by usage KPIs. Increase only as off‑chain revenue and retention confirm durability beyond emissions.
8) KPIs to demand before scaling position
Adoption & monetization
≥ 1,000 paying B2C users with >25% 90‑day retention; ≥ 3 enterprise API customers (named or auditable).
Accuracy & robustness
Third‑party blinded evaluation on fresh corpora (not present in training/leaderboards), with FPR targets per segment (e.g., ≤1% in EDU).
Go‑to‑market
Moodle/Google Classroom integrations launched with initial institutional logos; public security/data‑handling docs.
On‑chain health
Stable UID utilization, validator diversity, and consistent emissions share under dTAO/flow.
9) Scenario analysis (12–18 months)
Bull case (30%)
EDU and newsroom filters adopt; API volume + 5–10 mid‑market customers; revenue offsets inference and dev costs; alpha demand rises as builders stake for validator yield + usage. Benchmarks remain top‑tier.
Base case (55%)
Solid B2C, few enterprise pilots; modest EDU traction post‑plugin; alpha returns track overall TAO/dTAO flows; SN32 remains a steady mid‑tier emissions earner with slow but real off‑chain MRR.
Bear case (15%)
Evasion tactics and rewriting tools compress detector edge; regulators/institutions de‑emphasize detection; usage shifts to style‑polish (“Britishizer”) and plagiarism—lower monetization; emissions share falls as flows rotate.
10) Key risks
+ Benchmark vs. real‑world gap. SOTA on RAID/GRiD/CUDRT is encouraging but fresh distribution shifts and prompt obfuscations can degrade accuracy; insist on blinded, out‑of‑sample tests with reporting by domain.
+ False‑positive optics. EDU/HR misuse brings reputational/legal blowback even at low FPR; SN32’s low‑FPR stance is right but must be proven in the wild and explained well.
+ Arms race dynamics. As frontier LLMs evolve, detector shelf‑life shortens; SN32 must continuously refresh training data/attacks (their docs suggest this).
+ Token microstructure. Alpha pools can be thin; slippage risk on entries/exits; emissions under flow mean returns can change quickly as stake rotates between subnets.
11) Concrete diligence steps (2–3 weeks)
Product audit: run large blinded evaluation across domains (news, student essays, social, code comments) and measure FPR/TPR by cohort; compare to GPTZero/CopyLeaks.
Integration test: exercise the API at volume; check latency, rate limits (site lists 2,000+ texts/min for enterprise), and cost per 1k tokens/words equivalent.
Institutional pilots: request Moodle/Google Classroom timelines and 1–2 EDU letters of intent; verify data‑handling/PII policy.
12) SWOT (succinct)
Strengths: Top benchmark results; clear app + API; public pricing; continuous validator‑driven improvement; multilingual claims; explainable outputs.
Weaknesses: Team depth public info limited; detection remains adversarial; LMS/enterprise integrations still upcoming.
Opportunities: EDU integrity tooling, newsroom/content moderation, compliance filters; become the default detection layer other subnets/apps call via API.
Threats: Rapid LLM evolution; rewriting/evasion tools; regulatory skepticism of automated judgments; alpha market thinness.
13) Narrative examples (to make it tangible)
University workflow. A professor bulk‑uploads 120 essays. The deep scan flags 14 with high AI likelihood and pinpoints suspect sentences; the LMS plugin attaches a report and FPR‑calibrated disclaimers. The professor chooses coaching over punishment in borderline cases—policy aligned with low‑FPR ethos.
Publisher filter. A magazine’s CMS routes freelance submissions through It’s AI’s API. Submissions with high scores get an editor review, not an auto‑reject. Over a quarter, the team reduces editorial time on spam/LLM‑boilerplate by 30% without alienating writers.
---
14) Bottom line
Recommendation: Constructive with evidence gates.
SN32 is one of the clearest revenue‑ready stories in Bittensor: a live product with transparent pricing, measurable benchmarks, and obvious customer segments. Treat the initial position as a research ticket sized to on‑chain liquidity, then increase allocation only after external KPIs (paying users, API logos, EDU integrations) are met. This approach aligns with disciplined macro‑fund + crypto‑native risk management and the post‑dTAO reality that cash‑generating subnets deserve larger weights.
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World Schools Summit
Next station after GESS conference was world schools summit at Abu Dhabi organised by T4 Education, where we were connecting with schools representatives and continued our integration into local Educational sector.
I was caught on 4k w this guy at the bar on someone’s story 🥀 #itsai
Did you know Professor Stephen Hawking released an album of covers a few years before he died?
Well, he definitely did 😐
Here's the proof.
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#itsai
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