Ab Intl Group Corp (ABQQ)

$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/

$ABQQ Jan 16 is when the D comes off and the symbol changes to AERA. This will allow many more market makers into this stock! The Company estimated its 8/31/26 net income will be $2.7 million on about $13 million in sales. My gut is closer to $4 million and $1 shr in earnings! Either way earnings are surging. This Agentic Ai company is among the most undervalued companies trading today. I believe that the tick tock creators and only fans creators will figure out how easy it is to take what they have already created for their other platforms, pay $10/ wk and turn their content into many 100 episode tv series that will generate significant income. It will take only 30 min to do this per idea. Even if it is lousy their 50% of the $4/ minute that AERA will generate huge revenue for them from the insatiable demand by LLM’s for content training of their inference training. This is going viral ! I am long and willing to trade at will!

$ABQQ This Company is very similar to a company I started recommending over 10 months ago called VTYX. Though in a different industry I found a particular thing the company did well on and jumped on it before the world began to realize how big nlrp3 inhibitor drugs would do. Here this is the only true agentic AI play making $ that is not buried in an older, high valued play where it gets diluted. It services the Creator market and in my opinion will be as complimentary to Tick Tock as nlrp3 ‘s are to Glp-1 drugs! This tech allows anyone with a cell phone to create a written script from disparate ideas and then turn it into a real series that can be watched and monetized with ads in 30 min! Tick Tock players can turn their efforts into this instantly. It cost only $10/ week and you get 50% of all ad revenue . Tubi and Pluto will need original content as well as other FAST players. The stock is trading at its 2026 eps forecast of $1/ shr. This could get a 50-100 P/e multiple. I am long!

$ABQQ is active again this session. ALDS confirmed shareholders will receive transaction details in its forthcoming SEC filing. https://finance.yahoo.com/news/applife-digital-solutions-inc-announces-134000740.html

$ABQQ Sod2’s analysis so how Samsung, Tubi and Roku’s free streaming channels have got nearly 6% of all streaming and growing exponentially. There are companies with direct access to these 3 or one one or two of them. ABQQ in my opinion has or is the process of cutting deals to distribute the good series they license thru these guys. Sansumg already reaches 90 million people in US thru their Tzv’s in use.

$ABQQ Assessment: ABQQ’s UFlix / Ufilm AI Platform On December 22, 2025, the company released a shareholder update explicitly marketing a new "Agentic AI" ecosystem called Ufilm AI (creation tool) and Uflix (streaming platform). The company has pivoted aggressively to this model, rebranding from AB International Group to AI Era Corp and executing a massive 1-for-2,000 reverse stock split in late December 2025 to restructure its shares. Here is a detailed expert assessment of the platform, its claims, and its value to FAST channels like Join TV or AITV. 1. The Claims vs. Reality Your Description ABQQ's Official Claim (Dec 2025) Technical Reality Assessment "100 episode series in 15 minutes" The PR claims it generates "full series (1–100 episodes)... in ~30 minutes." Technologically Improbable for High- End Video. In 2025, generating 100 episodes of broadcast-quality video in 30 minutes is not possible on a smartphone. ► Likely Output: These are almost certainly "Micro-Dramas" consisting of static AI images with subtle motion (Ken Burns effect), AI voiceovers, and background music—essentially motion comics or low-fidelity animations, not live- action video replacements. "Smartphone App" "Studio power in a smartphone... creators with just a smartphone." Plausible. If the heavy lifting is done in the cloud, a smartphone interface for text prompting is standard. "50/50 Revenue Split" Confirmed: "AI Era sharing 50% of revenue" with commercial users/creators. Standard Incentive. This matches the "Creator Economy" model used by TikTok and YouTube to attract volume. It costs ABQQ nothing if the content doesn't earn ad revenue. 2. Financial & Corporate Health (Due Diligence) • The "Reverse Split" Red Flag: In December 2025, the company executed a 1-for- 2,000 reverse split. This is a drastic financial maneuver usually done to artificially inflate a stock price that has crashed to near-zero (sub-penny levels) to remain listable on exchanges like the OTCQB. It often wipes out the equity value of long-term retail holders. • History of Pivots: ABQQ has a history of chasing "hot" trends to boost investor interest. o 2021: NFT Movie Marketplace. o 2024: Movie Distribution in China (AB Cinemas). o 2025: Agentic AI Micro-Dramas. o Assessment: This pattern suggests the "Agentic AI" pivot may be more about selling stock than revolutionizing media. 3. Value to FAST Channels (Join TV, AITV) You asked if this would be valuable to license to channels like Join TV or AITV. YES, but with major caveats. Why It Is Valuable (The "Pros") 1. 2. 3. Infinite Inventory: FAST channels are hungry for content to fill 24/7 linear schedules. A "100-episode series" (even if low quality) fills a 2-3 hour block perfectly. Zero Cost Basis: If ABQQ licenses this content on a revenue-share basis, Join TV pays $0 upfront. They only pay if ads are watched. This is infinitely better than paying $50,000 to license a B-movie that might flop. The "Vertical Drama" Trend: The "100-episode" format (1-2 mins each) is designed for the "TikTok generation" of attention spans. This is a format traditional TV currently lacks. Why It Is Risky (The "Cons") 1. 2. Quality Control: If the AI generates 100 episodes in 30 minutes, there is zero human oversight for quality. You risk airing episodes with AI "hallucinations" (warped faces, nonsensical dialogue) that could damage the reputation of the FAST channel. Audience Fatigue: While "Micro-Dramas" work on phones (vertical scroll), they often look terrible on a 65-inch TV (the primary device for FAST channels like Pluto/Tubi). The low resolution and static nature of "Ufilm" content might cause viewers to tune out immediately. 4. Strategic Verdict The UFlix/Ufilm platform is a "Quantity over Quality" engine. • For Creators: It is a lottery ticket. You type an idea, get a "show," and hope it goes viral. The 50/50 split is fair because the effort required is near-zero. • For Investors: It is a high-risk speculative play (Penny Stock). The 1:2000 split indicates previous shareholders lost nearly everything. • For Partners (Join TV/AITV): It is a low-risk experiment. They should only take this content on a non-exclusive, revenue-share basis. If it works, they get free ad revenue. If it fails, they cancel the feed. It is not a replacement for premium content like Star Trek or The Bachelor. • “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 securities and reflects my personal investment decisions.”

$ABQQ Based on the industry landscape in late 2025, the platform you are describing closely matches the capabilities of "Showrunner" (by Fable Studio) or a specialized Agentic Micro-Drama Platform (similar to Dashverse or QuickTV's AI pipelines) that targets the booming "vertical drama" market. While no single app perfectly delivers "100 viewable episodes in 15 minutes" at broadcast quality (rendering physics still requires more time), the business model and workflow you described are not only feasible but are actively reshaping the FAST (Free Ad-Supported Streaming TV) landscape. 1. What is this Platform? The platform falls into the category of Consumer-Grade Agentic Showrunners. • Closest Real-World Match: Showrunner (by Fable Studio) is the pioneer in this space. It allows users to "write" episodes by typing simple prompts, managing the AI agents (actors, cameras, editors) to produce the show. • The Format: The "100-episode series" structure strongly suggests AI Micro-Dramas (vertical, 1-2 minute episodes). Platforms like QuickTV and ReelShort popularized this format, and AI platforms like Dashverse now automate the production (script-to-video) to lower costs by 75- 90%.[1] • The Revenue Model: The 50/50 split is a growing standard for "AI-User Co-Creation." For example, ProRata and TikTok Pulse popularized 50/50 splits to incentivize creators to use their infrastructure.[2][3] 2. Is This Valuable to FAST Channels (Join TV, AITV)? Yes, this would be exceptionally valuable. FAST channels like Join TV and AITV operate on a volume-based ad model. Your described platform solves their three biggest problems: Cost, Churn, and Exclusivity. A. The "Inventory Engine" Value (Ad Volume) • Traditional TV: A 100-episode series costs millions and takes years. Ads can only run once the show is finished. • Your Platform: A 100-episode series generated in minutes creates hundreds of new ad slots almost instantly. • Why it works for Join TV/AITV: FAST channels need 24/7 content to fill their linear grids. A platform that turns a user's idea into a scheduled 100- episode block allows the channel to sell ad inventory immediately ("from the start," as you noted). B. Solving the "Rerun Trap" (Exclusivity) • Currently, most FAST channels (Pluto, Tubi) license the same old reruns (e.g., Hell’s Kitchen, Forensic Files). • Value Add: Your platform provides Original Programming at $0 licensing cost. If "Join TV" can market "Brand New AI Sci-Fi Series: Galactic Drift" instead of "Reruns of Andromeda," they gain a unique selling point that competitors cannot easily copy without the same AI tech. C. The "Micro-Drama" Engagement Hook • The "100 episode" count is typical for Micro-Dramas (apps like DramaBox or ReelShort). These are "sticky"—viewers watch 60 episodes in a row because they are short (1-2 mins) and end on cliffhangers. • Retention: This format has higher completion rates than 45-minute pilots. For a FAST channel, this means a user stays tuned for 2 hours straight, watching 30-40 ads, rather than tuning out after 10 minutes. 3. Strategic Recommendation If you are evaluating this for a partnership or investment with Join TV or AITV: 1. Quality vs. Volume: The "15-minute" turnaround likely produces "Good Enough" animation or static-image motion (like a motion comic) rather than Pixar-level video. This is acceptable for FAST. Data shows FAST audiences accept lower fidelity if the story (script) is engaging.[1] 2. The "50/50" Incentive: This is the "secret sauce." By splitting revenue, you turn millions of smartphone users into unpaid showrunners. You get the diversity of millions of human imaginations without hiring a single writer, while the user does the work of prompting and curating. Verdict: This platform effectively acts as a "Content Factory" for FAST channels. In a market where content is usually the biggest expense, a platform that makes content free (or revenue-shared) is a potential unicorn in the 2025 media economy. “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 securities and reflects my personal investment decisions.”

$ABQQ As of late 2025, free ad-supported streaming television (FAST) has cemented itself as a dominant force in the media landscape, capturing a record share of viewership that now rivals or exceeds many paid subscription services. Executive Summary: The State of Free Streaming in 2025 In 2025, FAST platforms collectively captured 5.7% of total US TV viewing time. To put this in perspective, this category now generates more viewership than any single broadcast network (such as NBC, ABC, or CBS) and has surpassed the viewing time of major paid services like Max and Peacock.[1][2] The market is dominated by three primary platforms—The Roku Channel, Tubi, and Pluto TV—which together account for the vast majority of this sector's 5.7% share. 1. US Market Share (2025) Note: All market share figures refer to the percentage of total TV usage (broadcast + cable + streaming) as measured by Nielsen's "The Gauge" in late 2025. YouTube is excluded. Platform Type US TV Market Share (Late 2025) Key 2025 Insight The Roku Channel OS / Device- Owned 2.9% [3][4] Leader. Hit a record high in Nov 2025; viewership rose 9% month-over-month. Tubi Media-Owned (Fox) 1.8% [1][5] Challenger. Surpassed paid services like Peacock (1.7%) and Max (1.6%). Pluto TV Media-Owned (Paramount) 1.0% [1] Stable. Consistently captures ~1% of all TV time; deeply integrated into Paramount's ecosystem. Other FAST Mixed (Samsung, Freevee, etc.) ~2.0% [1][6] Collective long-tail of services like Samsung TV Plus and Amazon Freevee. TOTAL FAST All Types 5.7% [1] Surpasses Broadcast Networks. Analysis of Types: • OS/Device-Owned (e.g., Roku, Samsung): These platforms leverage their control of the hardware (TV interface) to drive viewership. The Roku Channel's dominance (2.9%) proves the power of being the "home screen" application. • Media-Owned (e.g., Tubi, Pluto): These rely on massive content libraries from their parent companies (Fox and Paramount). Tubi's success (1.8%) is driven by a strategy of "cordless" younger audiences, while Pluto TV mimics traditional linear TV. 2. Worldwide Audiences (Global Reach) While "time spent" data is less standardized globally than in the US, Monthly Active Users (MAUs) serve as the primary metric for worldwide reach in 2025. • Tubi: ~97 Million MAUs. Tubi remains a leader in user count, expanding aggressively in the UK and Latin America. It is widely considered the most watched free streaming service in North America.[1] • Samsung TV Plus: 88 Million MAUs. In October 2025, Samsung announced it had surpassed 88 million active users, claiming the title of the "largest free streaming service" by user count, driven by its pre- installation on millions of Samsung Smart TVs globally.[7][8] • Pluto TV: 83 Million MAUs. As of mid-2025, Pluto TV reported 83 million global users, with strong footholds in Europe and Latin America due to Paramount's international infrastructure.[9] 3. Advertising Revenue Generated (2025) The FAST sector is a multi-billion dollar advertising engine. In 2025, the US FAST ad market alone is projected to reach approximately $5.78 billion, with total global ad spend on streaming reaching over $43 billion (though this includes paid ad-tiers like Netflix/Disney+).[10][11] Revenue by Platform • The Roku Channel (Roku Inc.): o Revenue: Roku's "Platform" revenue (which is primarily advertising) reached $1.065 billion in Q3 2025 alone (+17% YoY).[12] o Projection: Full-year 2025 Platform revenue is projected at ~$4.11 billion. While this includes ad-tech fees from other apps, The Roku Channel is the primary driver of this inventory.[13] o Trend: Roku has successfully monetized its 2.9% market share by selling "home screen" ads and interactive video spots. • Tubi (Fox Corp): o Revenue: Tubi generated >$1 billion in annual revenue in 2025. In Q3 2025 (Fiscal Q1 2026), Tubi's revenue surged 27%, a growth rate significantly higher than the broader ad market.[5][14][15] o Milestone: Tubi achieved profitability for the first time in Q3 2025, a rare feat in the streaming world where many services still operate at a loss.[16][5] • Pluto TV (Paramount Global): o Revenue: Pluto TV contributes to Paramount's Direct-to-Consumer (DTC) segment, which reported $2.04 billion in revenue in Q1 2025.[17] o Ad Specifics: Paramount's DTC advertising revenue was nearly $500 million in Q2 2025. While this bucket includes the ad-supported tier of Paramount+, Pluto TV remains a critical volume driver for this revenue stream.[9] 4. Summary of "Types" The 2025 market clearly distinguishes between two successful types of free streamers: 1. The Aggregators (OS-Based): Platforms like The Roku Channel and Samsung TV Plus are winning by ubiquity. They don't need to acquire users; the users effectively "buy" the platform when they purchase the TV. This has allowed Samsung to reach 88M users and Roku to capture nearly 3% of all US TV time. 2. The Libraries (Content-Based): Platforms like Tubi and Pluto TV win by depth of content. Tubi's 27% revenue surge proves that a massive, algorithmically driven library of movies and shows can retain users just as well as expensive original programming. “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 securities and reflects my personal investment decisions.”

$ABQQ cont: so the volume of driers will explode as individuals through word of mouth begin to use this to over $2 million/ yr just texting into the AI to create volume scripts that will generate that revenue. Assume 5% of this amount is good enough to stream on Tubi, Pluto and all the free services. Those free services with ads are exploding as well as YouTube. YouTube ad revenue already accounts for 45% of all streaming revenue all do to ads. This AI places the ads automatically not post production. This is production line content. This tech will scale. There are over 220 million creators but billions of people. If this goes viral AI ERA earnings explodes without even counting its AD revenue. Each person could represent over $2 mill per yr of rev and say 1.7 mill of earnings, only from LLM training! That is $.$.40/ shr per person. At 1000 people that is $400/ shr in earnings from 1000 full dedicated people. Insane potential.! I am long and will trade at will!