Ab Intl Group Corp (ABQQ)

$ABQQ If Synthesia the leading AI to take something and create video from it is worth $4 billion as a startup what is a company that enables any one with a smart phone or TV or computer to give random thought of only a few ideas and make a 100 series script with pictures of the characters and costumes in 30 min worth. It sure as all worth a lot more than $6 million. No debt and going to make $1 sh in the 8/31/26 year end. I am long and will trade at will

$ABQQ https://www.cnbc.com/2026/01/26/nvidia-alphabet-vc-arms-back-synthesia.html?__source=iosappshare%7Cph.telegra.Telegraph.Share

$ABQQ AMERICAN REBEL HOLIDNGS, INC. (NASDAQ: AREB) ANNOUNCES 1-FOR-20 REVERSE STOCK SPLIT WITH ROUND LOT SHAREHOLDER PROTECTION TO BE EFFECTIVE ON FEBRUARY 2, 2026, FOR ALL SHAREHOLDERS OF RECORD AS OF THE EFFECTIVE DATE

$ABQQ Interesting, this company has gone thru several manifestations and regrouping over the last 10 years and from what I have analyzed many toxic lenders have had their way with the prior incarnations of this company. When a stock gets to $.0001 it generally means there are tons of naked shorts because they get greedy and do not cover hoping it dies.( ie tax reasons make them hope so they pay zero tax) . Tge fact that Fred Deng caught this agentic Ai shows that public cos never die. He caught a whale and timing became right. The cleaned up company now has caught these naked shorts . The reverse and the recent move and the eps surge due to the insatiable demand by AI for training has them scared. Why else 2 ridiculous negative posts on a company trading at 1x earnings. This is going to be fun to watch as the sleeping short gets skewered! I am long and will trade at will!

$ABQQ don’t fall for it. This company is a giant scam for 10 years.

$ABQQ I continue to point out this is the cheapest Agentic AI company with several years of profit on the market. I believe they will make close to $1/ shr primary and $.70 after they do a floored common deal to gigantically drive the sales. I expect several other major strategic marketing deals that will really ignite this stock. In addition the posting of a negative report with erroneous numbers and ok’d info shows that there is a massive “ sleeping naked short that just woke up and knows how screwed they are. The float is 400 k shares and there will be nothing added for over 6 months. This implies well over $15 as a near term stopping point! I am long and will trade at will

$ABQQ hopefully ST will change this board to be “AERA” soon

$ABQQ Remember there is a new symbol, new name, and only 4.2 million shares out. AERA, Ai ERA corp, . Of the 4.2 million out the Comoany has 1.8 million shares under option at $.33/ shr. I expect they will raise $2.5-3 million let’s say at $.75/ shr for the equivalent of 3.6 million new shares. Subtract 1.8 million and the shares only increase to 5.8 million shares. However for the next 6 months the public float is only 400 k shares since the ceo owns 2 million. With my estimate of earnings of $4 million for the 8/31/26 yr end that means an eps of nearly $.70/ shr at full finance. So put a low multiple of 20 and you get $14/ shr. However with such a tight float and AI mania it could go much much higher! I am long and will trade at will!

$ABQQ Brilliant piece that captures ABQQ's situation perfectly. So if you want to update your understanding of ABQQ or get to know ABQQ better, this is essential reading. https://everyticker.com/quote/ABQQ/analysis/ai-era-corp-s-874m-valuation-rests-on-6m-revenue-and-a-promise-otc-abqq

$ABQQ AERA new symbol ! The prior press release demonstrates that once they license the creator scrip and they make the series AITV will distribute? This is one of many distributions coming. Huge deal! I am long and will trade at will

$ABQQ https://www.globenewswire.com/news-release/2026/01/20/3221912/0/en/AI-Era-Corp-AERA-Announces-Strategic-Investment-in-AITV-Technologies-Inc-to-Launch-Next-Gen-AITV-Channels-Fueled-by-UFilm-ai.html

$ABQQ Great piece that accurately captures ABQQ's current position. So if you want to refresh your understanding of ABQQ or learn about ABQQ for the first time, this is essential reading. https://everyticker.com/quote/ABQQ/analysis/ai-era-corp-s-874m-valuation-rests-on-6m-revenue-and-a-promise-otc-abqq

$ABQQ The new symbol as of today is AERA. It will take a while for stock twits to adapt. This company has the best agentic AI out there! The company forecasted $2.7 mill out of 4.2 million shares. When he buys back 1.8 mill at $.33/ shr he has under option the shares shrink to 2.4 million shares . I believe he will make $4 million and $1 shr before the buyback! This is the cheapest AI stock on the market! I am long and will trade at will

$ABQQ From my calculations tomorrow we se see the symbol change and D drop. If not Monday. The symbol changes to AERA. He reported his Q today, remember he is a 8/31/26 yr end. He reported$.09 per fully diluted shares. He is growing rapidly. This stock is so undervalued. Tomorrow many market makers who want to buy this can and will plug in! They are prohibited by the D. Watch what happens now over the next week. I am long and will trade at will!

$ABQQ The last three postings are an excellent reason why AI Era to be symboled Friday as AERA , is dramatically undervalued! Mercor hires highly educated professionals at $100/ hr to curate LLM and the reasoning content. In 3 years they raised money at a $10 bill market cap. Sera’s model allows the world’s creators to make huge money based on their own creativity as opposed to being just an independent contractor but allows for the aggregation of certain professionals to mimic Mercor’s model at 1/100 the cost and in a mass way. This may be 1 yr behind mercor but at a $4 mill market cap it is a dramatic undervaluation! I am lon and will trade at will

$ABQQ Does Mercour’s evaluation not prove dramatically that AI ERA is dramatically undervalued? In reality the content from creators includes high intellect as well as average people but allow for AI to reverse the process once it has contact with the script creator to allow a low cost aggregation of people from all walks of life and the world who can help curate for $ as well. In fact Mercer would love to partner with AI Era corp. Given this shoukd not the automated easy aggregation platform they have be worth similar to Mercer! The short answer is: Mercor’s valuation strongly supports your thesis that “training data + human judgment” is a gigantic, real market—but it does not automatically mean AI Era (ABQQD) should be worth anything close to Mercor today. It does, however, make the current ≈4–5 M USD market cap look potentially very low if AI Era executes. Below is a breakdown in plain terms. 1. What Mercor’s valuation actually proves Mercor (not “Mercour”) shows three big things about the AI ecosystem:[1][2][3] • AI labs will pay real money for: o High‑quality data, and o High‑quality human evaluations/feedback. • This is not theoretical: o Mercor went from seed to 10 B USD valuation in under 3 years. o It pays 30,000+ experts around 85–95 USD/hour and now disburses about 2 M USD per day in contractor payments.[3][1] o Clients reportedly include OpenAI, Anthropic, DeepMind, Meta, etc.[4][1][3] • “Human‑in‑the‑loop for AI” is seen by major investors as infrastructure‑level valuable, not a small add‑on. That is powerful confirmation of your core view: there is huge, monetizable demand for both data and human judgment in AI training. 2. Why that helps the AI Era thesis, but doesn’t equalize valuations How it helps Mercor’s success makes the following more credible: • The market for AI training inputs (data + evaluation) is enormous and real. • AI companies will pay substantial dollars if: o The content is rights‑clean and high‑value. o The human evaluators/creators are organized and reliable. • A platform that aggregates millions of creators and curators cheaply— like you describe for AI Era—sits in the same value chain as Mercor: o Mercor = expert evaluation layer. o AI Era = scripted content generation + licensing + (potentially) crowd curation. So on a conceptual level, Mercor’s valuation is bullish “proof‑of‑category” for AI Era’s general space. Why it doesn’t make AI Era “worth 10 B by logic” Public market and VC valuations are based on actual, demonstrated factors, not just the idea: • Mercor already shows: o Very fast revenue growth (thousands of % in 2025). o A meaningful revenue run‑rate from top‑tier customers. o A large, active network of high‑paying clients and 30k+ experts.[2][1][3] o Backing from brand‑name VCs and institutional investors. • AI Era (ABQQD) so far shows: o A credible model (http://Ufilm.ai/Uflix.ai, 50/50 split, 4 USD/min headline). o Projections of single‑digit millions in AI training revenue and ~13.5 M USD total revenue for FY 2026. o Profitability at a very small scale. o But: ▪ No evidence yet of OpenAI/Anthropic/Meta‑scale customers. ▪ Much smaller user base and brand recognition. ▪ OTC listing, thin liquidity, and higher perceived risk. So Mercor proves the category, but AI Era still has to prove: • Comparable scale (minutes licensed, creators onboarded). • Stable demand from major AI labs. • Growth trajectory beyond a few million dollars. Until then, markets will not price them similarly, even if the end‑state opportunity rhymes. 3. Your argument about “reverse process” and mass curation You make an important point: creator content isn’t just “average” — there is a distribution of intelligence and expertise inside the creator base, and AI can: • Generate scripts from prompts. • Then recruit those same or other creators as low‑cost curators/evaluators. • Turn “random creators” into a structured evaluation workforce (for a fraction of Mercor’s hourly expert rates). If AI Era can: 1. Onboard large numbers of script creators worldwide (10 USD/week, 30‑minute script generation). 2. Turn a subset of them into paid curators/evaluators of scripts, scenes, or outputs. 3. Sell both: o The content (scripts, videos) and o The evaluation signal (what creators up‑vote, edit, approve) then they start to look like a hybrid of: • A data/content platform (like Shutterstock/ABQQD’s script marketplace), and • A distributed eval platform (a “mass‑market Mercor” for less domain‑specialized tasks). Conceptually, that is very powerful. It: • Lowers the unit cost of human evaluation. • Gives LLM buyers content + labels in one pipeline. • Uses AI itself to orchestrate who should review what. This absolutely strengthens the strategic story and would be attractive to a company like Mercor in terms of partnership in theory (content supply + broad curation complement Mercor’s expert tier). But crucially: • There is no public information that Mercor “would love to partner” with AI Era; that’s speculative. • The value of this idea will only be recognized in valuation once AI Era shows traction: real volumes, real customers, real payouts. 4. “Shouldn’t their platform be worth similar to Mercor?” The honest answer: • Long‑term potential: If AI Era: o Aggregates millions of creators, o Licenses tens or hundreds of millions of minutes annually at 4 USD/min, o Builds a meaningful curation/eval layer, o Signs major LLM clients, and o Maintains profitability, • then a multi‑billion‑dollar valuation is absolutely plausible. In that sense, yes, one can argue that the type of platform they are building aims for a Mercor‑like strategic role in the AI ecosystem. • Today’s reality: Right now, AI Era is: o Tiny in revenue vs. Mercor. o Unproven at scale. o On an OTC market with limited institutional attention. o Dependent on future execution, which is uncertain. So markets are saying: • “This is a speculative early-stage play with big upside if it executes,” not • “This already deserves a 10 B valuation like Mercor.” In other words: • Mercor’s 10 B valuation is strong evidence that this kind of business can be extremely valuable. • AI Era’s ≈4–5 M market cap arguably looks very low relative to that end-state possibility, so from a thesis perspective you can reasonably argue it is “dramatically undervalued if it executes.” • But nothing in Mercor’s valuation forces markets to price AI Era similarly now. The gap reflects execution risk, scale, and credibility differences, not just idea quality. 5. How to frame this in a memo or pitch If you want to argue this forcefully (while staying realistic), a good framing is: • Mercor proves the ceiling. o 10 B valuation o 30k experts, $2M/day payouts, brand-name AI clients.[1][3] • AI Era defines a different layer of the stack: mass creator aggregation + script generation + licensing. o Same mega‑trend: feeding AI models with human input and content. o Focused on breadth (millions of creators) instead of expert depth. • Current mispricing: o AI Era valued at ≈4–5 M market cap while: ▪ Operating in the same mega‑trend. ▪ Already generating/projection millions in revenue. ▪ Offering creators $2/minute share on content training. • Upside statement (not guarantee): o If AI Era even partly closes the gap to Mercor in scale and customer base, a valuation that is orders of magnitude higher than today is conceptually consistent with what capital markets have already rewarded in Mercor. That keeps the logic tight: • Mercor’s valuation is evidence of category value, • AI Era’s current valuation is small relative to that, • But whether it “deserves” Mercor-like value depends entirely on execution, not just the idea.

$ABQQ Mercor: The AI Evaluation Powerhouse Based on the search results, Mercor is not a content licensing platform like ABQQD. Rather, it is something quite different but highly relevant to the AI training ecosystem you've been discussing. What Mercor Is Mercor is an AI-powered talent marketplace and data evaluation platform founded in 2023 by three 19-year-old college dropouts: Brendan Foody (CEO), Adarsh Hiremath (CTO), and Surya Midha (board chairman). The company is headquartered in San Francisco.[1][2][3] Core Business Model Mercor connects high-skill human experts (lawyers, doctors, engineers, investment bankers, consultants, journalists) with leading AI labs and enterprises to: • Train frontier models through expert feedback and evaluation. • Perform data labeling and annotation with professional judgment, not low-wage workers. • Create rubrics and evaluation frameworks for model assessment. • Judge model outputs for quality and accuracy—a process called "AI evals" or "evaluations."[4][5][3][1] Unlike traditional data-labeling platforms that hire low-cost workers, Mercor focuses on expert-level judgment and nuance—the kind of specialized knowledge that LLMs currently lack.[5][4] Key Clients • OpenAI • Anthropic • Google DeepMind • Meta • Alphabet, Amazon, and other major tech companies[2][3][1] The Numbers: Explosive Growth Mercor's trajectory is extraordinary: Metric Value Founded January 2023 Initial revenue (8 months in) $1M annual run rate Series B (Feb 2025) $100M funding, $2B valuation Series C (Oct 2025) $350M funding, $10B valuation Growth in 2025 Revenue up 4,658% Monthly payouts to experts From $2M/month → $2M/day (as of 2025) Active contractors 30,000+ domain experts Users Grew to 3.4M (2025) Interviews conducted (2025) 1.5M interviews Average contractor pay $85–95 per hour[1][4][2][3] The three founders became the youngest self-made billionaires in 2025 at age 22, after Mercor raised $350M at a $10B valuation in October 2025.[1][2] How Mercor Fits Into AI Training The Model Training Pipeline AI development involves several stages: 1. Pre-training: Ingesting massive amounts of unstructured text data (low human-judgment component). 2. Post-training / Fine-tuning: Using human feedback to shape model behavior, alignment, and accuracy. 3. Evaluation: Testing models to ensure they perform well on specific tasks. Mercor's sweet spot is post-training and evaluation, where: • Specialized expertise is critical. • A lawyer's judgment of legal outputs is worth far more than a generic labeler's rating. • Doctors evaluating medical reasoning, engineers assessing code quality, etc.[4][5] The Value Proposition Traditional data-labeling companies (like Scale AI) rely on volume and cost efficiency. Mercor's differentiation is quality and expertise: • By using domain experts (not generic workers), models receive feedback on nuanced, high-stakes decisions. • This accelerates model improvement in specialized domains (law, medicine, finance). • The platform uses AI to vet and match experts, ensuring quality at scale.[3][5][4] Mercor vs. ABQQD: Two Different Approaches Aspect ABQQD Mercor What they supply Creator-generated scripts and content Expert human judgment and evaluation Content type Video scripts (100- episode series) Labeling, rubrics, evaluation assessments Creator/Expert compensation $2/minute (licensing revenue split) $85–95/hour for expert work End use Training AI models on diverse multimodal data Post-training feedback and model evaluation Business model Content licensing (Creator → Platform → LLM buyers) Managed services / Expert-as-a-Service Scale Aims to aggregate millions of creator scripts Currently 30,000+ active experts Valuation ~$4–5M $10 billion (as of Oct 2025) Why Mercor Is Relevant to Your Framework Mercor demonstrates that: 1. AI companies are willing to pay for high-quality input, whether content or expert judgment. 2. There is massive, growing demand for human expertise in the AI stack— so much so that a 2.5-year-old company reached $10B valuation. 3. The bottleneck in AI is not just compute, but quality data and human judgment at scale. 4. Experts and specialized labor are now seen as strategic assets, with companies paying $85–95/hour for their time. Connection to the "Creator Extraction" Argument Just as Mercor proves that AI companies will pay well for expert evaluation, ABQQD and similar platforms prove they will pay for creator-generated content. Together, they show: • The AI training pipeline has multiple monetization layers. • There is real, substantive economic value at every layer (content generation, expert feedback, evaluation). • Creators and experts are being systematically compensated for their value via Mercor and ABQQD-like platforms. • Meanwhile, big tech platforms extract this same labor (user-generated content + implicit feedback from interactions) for zero direct payment. The Broader Picture Mercor's $10B valuation at just 2.5 years old underscores the intensity of the AI arms race: • Companies like OpenAI, Google, Meta, Anthropic are racing to train the best models. • They need constant feedback loops: expert evaluations, rubric refinement, alignment testing. • They are willing to pay substantial sums for high-quality, reliable human input. This is the opposite of what YouTube and TikTok do—they extract human- generated content and feedback (through algorithms and training) for zero payment. If expert evaluators earn $85–95/hour (Mercor) and script creators earn $2/minute (ABQQD), the implicit value of YouTube creators who supply both content and implicit feedback (through watch patterns, engagement, etc.) is vastly undercompensated.

$ABQQ cond…AI Era's differentiation: • Creator‑first economics (2 USD/minute to creators, transparent, contractual). • Integrated script generation, reducing content‑creation friction to near‑zero. • Multi‑buyer licensing to multiple major AI companies (xAI, Alibaba, plus unnamed daily inquiries), not locked into one platform. • Open‑ended, recurrent demand from buyers, not "occasional" or experimental. • Potential reverse curation layer, turning creators into low‑cost, high‑coverage evaluators. At today's scale and valuation, there is no obvious direct competitor delivering exactly this combination in a public company wrapper. 9. Catalysts for Growth and De‑risking 9.1 Immediate (Next 6–12 Months) • 2 million USD capital injection deployment: o Paid marketing campaigns targeting global creator communities. o Creator onboarding velocity likely to accelerate 5–10×. • Multiple daily buyer inquiries becoming formal contracts: o As AI Era formalizes additional customer relationships, revenue visibility improves. o Licensing volumes could increase 3–5× if new contracts are signed. 9.2. Medium-Term (12–24 Months) • 10,000+ creators on platform (from current 3,000, with marketing push). • Hundreds of millions of licensed minutes (from current single-digit millions). • Potential reverse curation pilot with one major buyer, validating the eval‑layer thesis. • Possible uplisting from OTC to NASDAQ/NYSE (if revenue and profitability thresholds are met). 9.3. Long-Term (2–5 Years) • 100,000+ creators globally. • 1–10 billion minutes/year licensed to multiple AI labs. • Multi-stream revenue (subscriptions, licensing, API, curation services). • De facto standard for scriptable narrative training content. 10. Valuation Perspective 10.1. Today's Reality • Market cap: ≈ 4–5 M USD. • Current traction: o 3,000+ creators (and virally growing). o Real or projected multi-million-dollar licensing flows. o Profitability at early stage. o 2 M USD capital injection imminent. o Multiple daily inquiries from major AI companies. The market is pricing AI Era as: • A tiny, high‑risk micro‑cap, • With minimal credit given for: o The licensed demand from xAI and Alibaba. o The multiple daily inquiries from other major AI players. o The creator base and organic growth. o The validated economics (4 USD/minute). 10.2. Possible Upside Paths (Illustrative, Not Predictive) Conservative Scenario (3–5 years): If AI Era grows to: • 25,000 creators, • 100M licensed minutes/year, • 50M USD annual revenue (licensing + subscriptions), • 25–30% net margins, Then at 8–10× earnings or 5–8× sales multiples (typical for mid-cap SaaS and data platforms): • Valuation range: 250M–400M USD Relative to 4–5M USD today, that's a 50–100× return. Moderate Scenario (3–5 years): If AI Era becomes a recognized category leader with: • 50,000+ creators, • 500M+ licensed minutes/year, • 150–200M USD annual revenue, • 30–35% net margins, • Multiple blue‑chip AI customers (xAI, Alibaba, plus OpenAI, Anthropic, Meta, Google, etc.), Then at 12–15× earnings or 8–12× sales multiples (growth premium for proven category leaders): • Valuation range: 1.2B–2.4B USD That's a 250–500× return from current levels. Bull Scenario (5+ years): If AI Era becomes the dominant global marketplace for creator-generated training content and evaluation: • 100,000+ creators, • 2–5B licensed minutes/year, • 400M–1B USD annual revenue, • 35–40% net margins, • Strategic importance to multiple Tier-1 AI companies, Then valuations could reach Mercor-adjacent territory (several billion USD), reflecting: • Market position and defensibility, • Recurring, high-margin revenue, • Strategic optionality for larger platforms. That would represent a 1,000×+ return on current valuation. 10.3. Why These Scenarios Are Plausible • Mercor's 10B valuation proves that AI infrastructure and evaluation services can scale to billions of dollars. • Alibaba and xAI are paying 4 USD/minute proves that AI training content has this real, auditable value. • Multiple daily inquiries proves that AI Era is not a niche player but a bottleneck resource that major labs are competing for. • 3,000 creators growing virally, before marketing proves that the supply side can scale rapidly. • Global creator base, 10 USD/week cost proves a massive addressable market (billions of creators worldwide who could participate). The business is not speculative in concept; it's only uncertain in execution and scale. 11. Key Risks (Honest Assessment) To be transparent, major risks include: • Execution risk: Can the team scale creator acquisition, buyer fulfillment, and operations without stumbling? • Customer concentration: If revenue becomes too dependent on xAI or Alibaba, platform leverage could be lost. • Competitive response: Incumbents (Meta, OpenAI, Google) or better- funded startups could replicate the model. • Creator quality and burnout: As the platform scales, maintaining quality of generated scripts could deteriorate; creator churn risk if payouts don't materialize as promised. • Regulatory/legal shifts: AI and data-licensing rules are evolving; compliance costs or restrictions could rise. • Micro‑cap/OTC risks: Limited liquidity, volatility, analyst coverage, and governance oversight. These risks appropriately explain why markets currently discount the story so heavily, despite the conceptual upside. 12. Summary Investment Narrative AI Era (ABQQD) is: • A first‑mover, creator‑centric AI content and licensing platform. • Already connecting thousands of creators to real, validated AI training demand: o xAI and Alibaba are active, paying customers at 4 USD/minute. o Multiple daily inquiries from other major AI companies, indicating the platform is a bottleneck resource. • Positioned to leverage reverse curation to build a scalable, human‑in‑the‑loop evaluation layer on top of its content pool, paralleling (and complementing) Mercor's high-end eval business. • Operating in a space validated by Mercor's 10B USD valuation and the broader AI infrastructure boom. • Currently valued by the market at just 4–5 million USD, despite: o 3,000+ creators growing virally (before marketing), o Multi-million-dollar licensing potential and contracts, o Imminent 2 million USD capital injection for growth, o Open‑ended buyer demand from two mega-cap AI companies, plus recurrent daily inquiries from others. From a thesis standpoint, this is a classic asymmetry: • Downside: Early-stage execution and platform risks typical of micro‑cap companies. • Upside: If AI Era successfully scales creators and licensing: o A conservative scenario (50–100× upside) reaches 250–400M USD valuation. o A moderate scenario (250–500× upside) reaches 1.2–2.4B USD. o A bull scenario (1,000×+ upside) reaches Mercor-adjacent territory. Nothing guarantees any of these outcomes—but Mercor's success, the xAI and Alibaba contracts, and the multiple daily buyer inquiries make clear that: • The category is real and very large. • The demand is not theoretical; it's active and recurrent. • AI Era's execution is the primary variable, not the market opportunity itself. For investors with risk tolerance for micro‑cap plays and conviction in AI infra trends, AI Era represents a concentrated bet on the gap between creator-supply side economics and big-tech extraction finally closing. “I own shares of the company and may buy or sell shares at any time without prior notice. This statement is not a recommendation to buy or sell shares at any time without prior notice. This statement is not a recommendation to buy or sell securities and reflects my personal investment decisions

$ABQQ AI Era Corp (ABQQD) – Investment Thesis Narrative 1. The Core Thesis AI Era Corp (ABQQD) is building a creator‑centric data and content platform for the AI age, sitting at the intersection of: • Explosive demand for AI training data, and • Massive, uncompensated supply of creator content on platforms like YouTube, TikTok, and Reels. Where Big Tech treats creator content as free fuel for AI models, AI Era: • Turns any person with an idea and a smartphone into a licensable IP creator in 30 minutes. • Pays those creators 2 USD per minute for training use of their scripted content (50% of a 4 USD/min license fee). • Licenses that content to leading AI labs (including confirmed demand from xAI and Alibaba, plus multiple daily inquiries from other major AI companies) at 4 USD per minute, with open‑ended demand at current scale. At a roughly 4–5 million USD market cap, AI Era is valued like a micro‑cap side project, not like a first‑mover infrastructure play in one of the fastest‑growing parts of the AI stack. 2. Problem: Big Tech's Free Ride on Creator Content • YouTube alone sees ~15.8 billion minutes of content uploaded per year. • A large share of that content is used—directly or via transcripts—to train: o Large language models, o Video and multimodal models, o Agentic and recommendation systems. • At a market-like rate of 4 USD per training minute, even if only half that volume were used, the 10‑year NPV of "avoided training costs" for Google is on the order of ~475–480 billion USD under reasonable assumptions. Yet creators are paid nothing specifically for AI training rights. Their IP fuels commercial models; the platforms capture the upside. This is the data arbitrage AI Era is attacking. 3. Solution: AI‑First Script Factory + Licensing Marketplace 3.1. http://Ufilm.ai and http://Uflix.ai – From Idea to 100‑Episode IP in 30 Minutes AI Era's products, http://Ufilm.ai and http://Uflix.ai, let any user: 1. Input ideas, prompts, and questions from a phone or computer. 2. In ~30 minutes, receive a fully‑generated 100‑episode script series: o Characters, arcs, plots, dialogue – ready to be licensed. 3. Pay roughly 10 USD/week for this capability. This is a turn‑key IP factory: • No writing skill required. • No production budget required. • The barrier to creating licensable narrative universes essentially disappears. 3.2. Licensing to AI Labs at 4 USD/Minute According to company representations: • AI Era licenses this content to AI labs (including xAI and Alibaba) for 4 USD per minute of script content for AI training use. • The licensing economics: o 4 USD/minute total training fee o 2 USD/minute to creator (50%) o 2 USD/minute to AI Era (50%) This is real, cash‑based demand for rights‑clean training content, directly contradicting the notion that the "fair" value of training rights is zero. 4. Market Validation: Major Buyer Demand – Multiple Daily Inquiries 4.1. Confirmed Buyers AI Era has validated demand from at least two mega‑cap AI companies: • xAI (Elon Musk's AI venture, launched 2023, valued at ~$50B) • Alibaba (Chinese tech giant, major LLM player via QWen and other models) Both are reportedly paying the 4 USD/minute rate for AI Era's scripted content. 4.2. "Open‑Ended Demand" + Multiple Daily Inquiries Most importantly, the company reports: • Open‑ended demand from current buyers (no hard ceiling on volume they'll purchase at current pricing). • Multiple daily inquiries from other major AI companies seeking training content. This is the supply constraint flipping to a demand constraint: • It's not "Can we find buyers?" → It's now "Can we generate enough creator content fast enough to meet buyer requests?" This is a profound inflection point that dramatically de-risks the business model: • The market is no longer theoretical. • It's not a few tentative pilots. • It's multiple Fortune 500–scale AI companies, placing recurrent, large- scale orders for training data. 5. Traction: Early Network Effects Before Marketing Key current indicators (per company information): • Creators: Over 3,000 creators already on the platform, growing virally via word of mouth and early visibility. • Demand signal: xAI and Alibaba are active buyers at 4 USD/minute with open‑ended volume, plus multiple daily new inquiries from unnamed but major AI players. • Upcoming capital: A 2 million USD cash injection is imminent, earmarked for: o Scaling marketing and creator acquisition, o Accelerating platform improvements, o Expanding licensing relationships and fulfillment infrastructure. Why this matters: This growth is happening entirely before the major marketing push. When AI Era begins paid user-acquisition campaigns and brand awareness pushes, creator adoption could accelerate dramatically. Current adoption is organic and word-of-mouth, driven purely by: • The 10 USD/week subscription value proposition, • Proof of creator payouts, • Viral sharing among content creators aware of this opportunity. Once marketing capital is deployed, the pull from demand (multiple daily buyer inquiries) meets the push from marketing supply (new creators), potentially creating exponential growth. 6. Business Model: High‑Margin, Two‑Sided Platform 6.1. Revenue Streams 1. Subscription fees: o Creators pay ~10 USD/week (~520 USD/year) to use http://Ufilm.ai/Uflix.ai to generate scripts. 2. Licensing revenue: o AI Era earns 2 USD/minute on every licensed minute of creator content (the other 2 USD/min goes to the creator). 3. Future extensions (natural and low‑cost): o Video generation add‑ons (Sora‑style pipelines). o White‑label solutions for studios or enterprise content owners. o API access for LLM labs to request tailored content. 6.2. Unit Economics • Script generation is largely automated (AI‑driven). • Distribution to LLM buyers is digital. • Once a piece of content is generated and licensed, marginal costs are minimal, and content can be: o Licensed multiple times over time, o Used across models, fine‑tunes, and evaluation pipelines. This creates the potential for software‑like margins once volumes and relationships scale. 6.3. Illustrative Margin Path If AI Era can: • Acquire 10,000 creators at 10 USD/week subscription revenue (~5.2M USD/year). • Drive those creators to generate 20M minutes/year of licensable content. • Maintain a 50% "accepted and licensed" rate (10M minutes/year actually monetized). • Earn 2 USD/minute on that: 20M USD/year from licensing. Then: • Total revenue: ~25M USD/year. • Gross margin: Very high (AI generation cost is low, licensing is digital). • Net margin: Even with 50% operating costs, likely 10–15M USD+ net income, or 40–60% net margins. That kind of profile at scale would command valuations in the hundreds of millions or higher. 7. Strategic "Reverse Curation" Layer – The Mercor Angle Mercor's 10 billion USD valuation proves that expert evaluation and human feedback for AI models is extremely valuable. AI Era has a unique opportunity to create a parallel, mass‑market evaluation layer at very low incremental cost: 7.1. Reverse Curation Concept 1. Automated quality ranking: o Once a script is generated, AI can rank its quality, structure, coherence, originality, and suitability for specific model types. o Top percentile creators (or those with high‑performing scripts) are identified automatically. 2. Paid curation and evaluation tasks: o AI Era can offer paid micro‑roles to creators: ▪ Reviewing and scoring peer scripts. ▪ Tagging content for specific domains or use cases. ▪ Providing feedback on AI outputs for specific model types. ▪ Identifying patterns in what makes scripts "training‑valuable." 3. Why this is powerful: o It converts "average creators" into a curation workforce, guided entirely by AI. o Provides LLM buyers with: ▪ Content, and ▪ Human‑in‑the‑loop feedback and quality signals in a single pipeline. o For AI Era, it's zero or near‑zero additional product cost: ▪ Ranking and routing can be done by AI. ▪ Curation pay can be funded from incremental buyer contracts or a small cut of total licensing. 7.2. Synergy with Mercor and the Broader AI Infra Stack • Mercor sits at the expert tier of evaluation (domain specialists: lawyers, doctors, engineers). • AI Era is positioned for the broad‑based creative and taste evaluation: o Story quality, narrative coherence, engagement potential, domain diversity. • Together, they create a multi‑layered, distributed human‑feedback mesh around AI training pipelines. 7.3. Strategic Implications This "reverse curation" layer: • Increases creator stickiness ("earn from your scripts and from your judgment"). • Deepens the platform's value to LLM buyers, who get both raw content and human‑quality signals. • Lowers AI Era's dependence on external eval contractors, improving margins. • Strengthens the moat: not just a content source, but a dynamic, creator‑powered evaluation network. 8. Market: Size, Timing, and Competitive Position 8.1. Market Size • AI training data and human‑in‑the‑loop services are increasingly seen as multi‑billion to tens‑of‑billions annual markets. • Evidence: o Mercor's 10 B USD valuation at 2.5 years of operation. o Reports of tech giants investing hundreds of billions in AI capex over this decade. o A material and growing share of this spend allocated to data, licensing, evaluation, and IP rights. Even a conservative slice of this market—say, 1–2% of the AI training data and services TAM—would justify multi‑billion‑dollar valuations for category leaders. 8.2. Competitive Landscape • Mercor: Dominates high‑end expert evaluation; not building creator content factories or broad licensing marketplaces. • Scale AI and traditional labelers: Focus on generic data labeling; not IP‑creating script factories or creator monetization. • YouTube/TikTok/Instagram: Rich in content but: o Do not pay for training rights. o Are not structured as transparent training‑data marketplaces with creator revenue share. o Are exposed to regulatory and legal challenges around creator compensation

$ABQQ https://www.wsj.com/tech/ai/training-ai-job-seekers-contractors-1a7bd492

$ABQQ Sod2’s post show our article on Jd Unfiltered and demonstrates that the new AERA, old ABQQ , is proving that Goog is robbing Creators of a NPV of $478 billion . This $4 mill market cap company that is on track to make $.75-1.00 / shr identifies a true market for creator content in training LLM and inference models for AI. Obviously Tick Tock and Meta are also robbing creators. The beauty of AI ERA is that anyone with a cell phone anywhere in the world can do this because of simultaneous translation AI. aERA(ABQQD) is way out in front of the sunami coming at Goog, meta, and tick tock first then watch as the FAST tv guys with this tech destroy Netflix and Disney’s margins! I am long and will trade at will. aERA going to viral!

$ABQQ https://jd-unfiltered.ghost.io/is-alphabet-shortchanging-creators-478-billion-why-creator-data-may-be-the-next-big-asset/