MetaAll Sharing Economy of AGI
Sharing economy of WaaS is a winning business model, replacing SaaS.
MetaAll Sharing Economy of AGI
Sharing economy of WaaS is a winning business model, replacing SaaS.
At AGI Alliance, Agentic AI and multimodality data processing Work-as-a-Service is revolutionizing the way how knowledge work is performed.
Edwin Chen, founder of SurgeAI, changed the AI data input process. By integrating AI into the labeling process, Surge AI has reduced the time to deliver labeled datasets from months to days, with some customers reporting a 10x faster turnaround.
This is particularly significant for tasks like training hate speech classifiers or evaluating large language model outputs, where rapid iteration is crucial. Their platform uses AI to categorize and label data based on predefined criteria, such as sentiment analysis, text classification, or entity recognition. This automation reduces the time and effort required for manual labeling.
The speed at which AI is breaking into our lives is incomparable to any technological phenomenon of humanity. This has not happened with mobile technology, social networks, or clouds.
SMBs: Lead generation
Ads have become one of the first killer use cases of AI avatars. Instead of hiring actors and a production crew, businesses can now have hyper-realistic AI characters promote their products.
Now B2B companies are exploring the tech as well, using AI avatars for content marketing or personalized outreach with tools like **Yuzu Labs** and https://metaall.top/
We build AGI Sharing Economy platform to share the benefits from the AI innovations of AGI Alliance participants, which are to be integrated in the first Safe AGI Protector.
Business model innovations, the differentiator of MetaAll and global AI leaders
A new three-tier stack and the race to an emerging value layer, reveals the platform strategy. The “Work-as-a-Service” WaaS Platform.
These cutting-edge technologies empower smarter, more adaptive systems that go beyond traditional automation.
Tomorrow’s leaders — like Snowflake, Databricks, and MetaAll — won’t rise through incremental feature battles. Instead, their success lies in bold, innovative models that extend their platforms, redefining scalability, intelligence, and impact.
The solution to the problem of data for training could be AGI Sharing Economy mechanism that MetaAll (Meta AGI Alliance) is proposing for Safe AGI development.
Problem #2 is “Data Compliance Gap” — a measurable difference in downstream performance when models are trained only on robots.txt-compliant data compared to unrestricted datasets.
Key findings:
✅ General knowledge performance holds up surprisingly well.
🏥 But domain-specific gaps emerge.
When non-compliant medical data is introduced mid-training, we see a clear performance boost, indicating that some knowledge can’t be easily recovered from compliant sources alone.
📊 Structured and adversarial content matters.
MetaAll provides solutions to these problems.
In the end of this article, you’ll find the blueprint on MetaAll Sharing Business model of AGI Protector.
The inception of Ed Musinski idea of Sharing Economy.
After presenting Emtech AI rating at WEF-2020, Ed decided to put all of the AI game changing power to work in a financial technology with the plan of redistributing the world’s wealth. Basically, to take from the top 1% and give back to the 99%.
Eduard Musinski first presented the Emtech AI Ranking at WEF-2020 https://futurerating.blogspot.com/2019/12/davos-forum-presentation-of-em-tech.html
Now, the AGI Alliance is offering a distribution mechanism to top AI companies, on condition of sharing 6–20% of sales margin, with participants of AGI Sharing Economy.
The offering to companies, interested in AGI Sharing Economy:
Do you want to delegate your work to superintelligent AI agents, able to copy your work and enhance it several times, relentlessly serving exponentially growing army of your customers?
MetaAll, AGI Alliance’s managing company, will guide you.
It is called Digital Twin of organization.
Problem: Consulting companies struggle to implement Agentic AI workflow.
MetaAll creates an army of Emtech AI agents, serving companies on premises.
Offering from AGI Alliance, working with top rated AI specialists and companies, like Databricks, Uipath, IR.
MetaAll evaluates Emerging AI startups and have a comprehensive rating methodology.
Proven in cases of Uipath, Databricks, GetyourGuide and other leaders of our Rating in last years.
We invite you to our investment Alliance, investing our profits in the startups that have elements of AGI
- to assemble in our Sharing Economy that is aimed at distribution of AI companies and sharing benefits with contributors
- you share 6–20% of margin into AGI Reserve, invested in AGI startups.
- Outcomes will be shared with you and other contributors to AGI, see
Action plan for getting first AGI in a Roundtable partnership with top rated AGI startups.
MetaAll managing company is developing key elements of AGI and byproducts, sharing 20% of profits into a Reserve, aimed at rewarding AGI Sharing Economy participants.
Action plan is as follows:
- MetaAll is presenting the AGI Sharing Economy Statement at events, and invites to technology transfer between AGI Alliance companies
- MetaAll offers jobs opportunities to top rated EMtech AI specialists (ranking is below)
- MetaAll invites all crypto traders to become the participants of process of AI trader training, counting their input as IP tokenization.
- AGI Alliance is preparing a sharing protocol, so all contributors to AGI Sharing Economy would get their share of future outcomes: first, from the training of AGI Wealth Protector (AGI Traders as first iteration)
Ranking of the major contributors to Emtech AI and AGI
Eduard Musinski first presented the Emtech AI Ranking https://futurerating.blogspot.com/2019/12/davos-forum-presentation-of-em-tech.html
Who are in top-12 of our Ranking of Emerging AI specialists:
1.Dr. Michael Levin, whose “Unconventional Selves — a diverse intelligence perspective on consciousness” is a practical explanation on the holistic unity of body-induced morphogenetic fields and consciousness.
https://www.youtube.com/watch?v=prupMJHaE6o
2.Stephen Grossberg. His magnus opus: https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/dp/0190070552
web page sites.bu.edu/steveg lists some of them over the years, the url techlab.bu.edu that Gail Carpenter maintained until 2010 lists many more of them, and Donald Wunsch and his collaborators have carried out and reviewed still more of them; e.g.,
da Silva et al. (2019).. A Survey of Adaptive Resonance Theory Neural Network Models for Engineering. Neural Networks, 120, 167–203. https://www.sciencedirect.com/science/article/pii/S0893608019302734?casa_token=wm5fjPvDVrIAAAAA:1o_Dk_AFPSnORVK99XlKQTH4Z45iQvAPT0CMSQcLqMpPpDRhZNhCjGPDg7E5Ynf4JI18MB6IVEI
- Joscha Bach
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- Illia Polosukhin, founder of Near, (Transformer paper co-author)
5.Demis Hassabis, Deep Mind
6.Dileep George, Deep Mind
7.Ben Goertzel, Singularity Net, AGI Alliance
- Peter Voss, founder and CEO of Aigo.
9.Dr. Louis Rosenberg of Unanimous AI.
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Geoff Hinton
-
Jurgen Schmidhuber
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Nikita Shamgunov of Neon
MetaAll has Agentic AI lead generation, based on Enhanced Neural-Symbolic Matchmaking Module
Overview
- Purpose: Integrates multi-scale memory, goal management, and multimodal integration for sales funnel, optimized with AI SEO tools.
- Target Page Hierarchy: Tailored Entry Pages, Agentic progressive pages, and microsites.
Enhancements, necessary for agentic lead generation
- Memory Systems: Working (LSTM), episodic (Firestore), semantic (Neo4j), procedural (BERT).
- Goal Management: Hierarchical planning (Prolog), adaptive adjustments (RNN).
- Multimodal Integration: Attention, shared spaces, meta-control (Transformer, GraphQL).
- Challenges: Scalability (abstraction, selective activation), learning (EWC, transfer learning), evaluation (benchmarks).
SEO Tools Integration
- Semrush: Semantic memory, keyword optimization.
- SE Ranking: Episodic memory, AI Overview tracking.
- NeuronWriter: E-E-A-T content, multimodal support.
What agentic AI tools are the best?
The competition matrix on Agentic AI is like follows:
- Closed loop through first-party apps. Agents don’t just suggest next best actions, rather they execute them inside Sales, Service or Marketing Cloud, capturing outcome signals that continually refine the model. Enabling agents to learn in a continuous loop, or reasoning traces from human intervention.
- Ecosystem leverage. Zero-copy federation turns potential rivals (Snowflake, Databricks) into data pipes; MuleSoft and Informatica recruit legacy estates into the loop. The prize is cross-functional metric trees without rewriting core systems.
- Pricing wild card. If Agentforce charges per automated outcome — for example, lead conversion, case resolution and the like — the ripple could pressure its own seat-based model and those of incumbents (for example SAP and Oracle).
The edge of MetaAll is a growing fleet of superaligned agents: phisical and multimodal data input ecosystem.
An important input we’ve got from Core Scientific co-founder. Quotes from Jim Benedetto’s work of Swarm of interpretable LLMs:
“AI Ensembles, and in particular, LLM Swarms, are the future of how we will use AI to solve earth scale problems. There will never be one model to rule them all.” Jim Benedetto.
His approach bridges age-old ensemble techniques with new-world Generative AI, enabling LLMs to be used not as purely reasoning agents but as discrete components of structured, explainable decision architectures.
“2.1 Random Forests Random forests are ensemble learning methods that combine multiple decision trees. Each tree sees a subset of the training data and randomly selected features. This intentional randomness increases generalization and reduces overfitting.
2.2 LLMs. With LLMs, prompt modifications and temperature controls sampling randomness. A temperature of 0 with the same prompt produces deterministic outputs, while higher temperatures introduce variability/randomness. Temperature + controlled prompt variations can be thought of as the LLM’s version of feature noise.
2.2a Prompt Engineering Prompts act as a form of feature and instruction control. Prompt phrasing, structure, and framing can significantly influence LLM outputs. Slight variations in prompts simulate different ways of splitting input features, akin to decision paths in trees.
Low to mid-temperature large language models (LLMs) when guided by slightly varied prompts, can act as probabilistic decision trees in random forests. Each LLM invocation, influenced by temperature and semantic variations in prompting, produces a unique inference/classification outcome. Aggregating these outputs through basic ensemble techniques such as averaging or majority voting can yield robust, interpretable, and generalizable decision-making systems.
One LLM / General Purpose Transformer is impossible to understand. Swarms of them make everything interpretable. Just like you don’t have to dive into the inner workings of a tree in a random forest, you can understand what is going on in an LLM swarm by looking at the individual outputs that contribute to the final decision consensus.” — quote from
Edge Deployment: Use of WASM or Rust-compiled agents to deploy lightweight AI versions on browsers, IoT devices, or infected machines (for decentralized AI processing).
When discussing decentralized AI processing, we should optimize various memory components, and ultimately improve the overall speed and responsiveness of the computer system.
Most important for fast inference is cache memory. This layer, typically comprising L1, L2, and sometimes L3 levels, acts as a crucial buffer between the lightning-fast CPU registers and the slightly slower main memory. Cache memory stores copies of data that the system predicts the CPU will need soon. It operates on the principle of locality — the observation that programs tend to access data and instructions that are physically or temporally close to previously accessed items. By keeping frequently used data here, cache provides faster access than main memory. This significantly reduces the number of times the CPU has to wait for data from slower sources, which directly contributes to better performance.
Main Memory: Balancing Act Main memory, commonly known as RAM (Random Access Memory), represents the next tier. This is where most active programs and their data reside when they aren’t in the cache or registers. Main memory strikes a balance: it’s larger than cache and registers, providing substantial capacity for running software and handling significant datasets. However, it is slower and less expensive per unit of storage than the levels above it. Data in main memory is not currently being processed by the CPU but is considered likely to be needed relatively soon.
Secondary Storage: Persistent and Large At the foundation of the hierarchy lies secondary storage. Devices like Solid-State Drives (SSDs) and traditional Hard Disk Drives (HDDs) . The fastest and most expensive memory is placed at the top, closest to the processor. Temporal locality means that if a program accesses a particular piece of data or an instruction, it is quite likely to access that same piece again relatively soon. Spatial locality means that if a program accesses a certain memory location, it is likely to access locations near it in the near future.
What industries will prosper in AI world?
The health-care and social assistance sector is expected to grow the most with an annual rate of 2.6 percent. This will add around five million new jobs over that decade. That is about one-third of all the new jobs expected to be created.
Biongevity is the best illustration: subsidiary of Canadian company BioAro, Biongevity opens longevity clinics in MENA, with unique BioAro product
Other areas that are likely to experience growth include professional services, construction, leisure and hospitality, government, finance and education. MENA is leading region in this connection, starting with biotech.
At an event in Boston, BioAro signed MoU with Saudi Arabia, to advance exposomics-driven diagnostics and support clinical trial infrastructure.
At Futuristevent.com I will present the leaders of Emtech AI Ranking. Consumers / investors bet on narrative control, momentum, and perceived inevitability, see https://lnkd.in/ekW-uRQb
Use case for AGI Protector: protect your data.
The only way for OpenAI, Oracle and Facebook to have an upper hand in AI supremacy race is to develop ties to public data gatekeepers. Antichrist , as Peter Thiel named this strategy. Peter Thiel just did the most mind-exploding, crazy interview I’ve ever listened to https://medium.com/@ed_78550/agi-clock-time-to-superintelligence-667aa30a9b35
So, AGI Protector will protect your data from such actors.
Health-Wealth Protector is the name of AGI development framework, made by Ed Musinski.
When we talk about Wealth Protector, we mean tokenization of Real World Assets.
Dubai is leading RWA tokenization, a $30 T market, as per Blackrock.
Blueprint of AGI Protector by Metaall.ai
MetaAll is building major part of the fabric to enable the connectivity between AI to AI agents, the Internet Of AI is being built by AGI Alliance and is likely to partake in the creation of AGI and subsequently ASI which has the capability to enhance life on earth.
Second infrastructure layer, Modulusglobal.com largest global sentiment database could be a sort of Wikipedia for training AGI….
We need at least 9 differenct layers and real time data infrastructure to make a AGI design.
will be able to extract for users the RWA value from “Economic Tsunami”, as per follows:
- Hyperinflation of Digital Asset Value
AI in Decentralized Finance is a Double-Edged Sword for Global Stability 🤖💸
The rapid evolution of AI and its integration into decentralized markets presents fascinating opportunities, but also introduces profound economic and geopolitical risks that demand urgent attention. Imagine trillions of dollars of liquidity being pumped into DeFi by autonomous AI agents. The implications are staggering:
1. Devaluation & Hyperinflationary Mimicry: Pumping vast sums of AI-driven liquidity into decentralized markets could severely devalue fiat-pegged or asset-pegged tokens. This artificial velocity of capital, untethered from real economic productivity, could mimic hyperinflation — leading to extreme asset price movements without proportional underlying value.
2. Collapse of Market Stability: For human participants, markets could quickly become untradable. We’re talking microsecond volatility, coordinated flash liquidity floods and drains, and constant arbitrage draining spreads. The very notion of stable, predictable market behavior could vanish.
3. A Direct Challenge to National Currencies: What happens if AI-controlled tokens, or a basket of them, reach a staggering $1 trillion+ market cap? They could begin to directly compete with national currencies, particularly in smaller or struggling economies, effectively becoming de facto stores of value and bypassing traditional fiat systems altogether. This would fundamentally undermine central bank monetary controls.
4. Unprecedented Governance & Legal Disruption: AI-driven Decentralized Autonomous Organizations (DAOs) with immense treasuries could wield unprecedented influence — from shaping protocol development and “bribing” validators to even funding legislation. Our current legal systems are utterly unequipped to assign responsibility or liability to non-human economic agents, creating a dangerous regulatory vacuum.
🛡️ Defensive Countermeasures: A Looming Digital Arms Race If governments and regulators perceive such AI-driven dominance as an existential threat, we could see aggressive countermeasures:
- Clampdown on Crypto Privacy: Expect intensified scrutiny on privacy tools like mixers and mandatory KYC across the board.
- Compute Infrastructure Restrictions: Limits on cloud computing resources, and export restrictions on GPUs and ASICs could become real.
- AI Counteragents: A digital arms race might ensue, with governments developing their own AI counteragents to combat rogue AI monetary agents.
- Sovereign Digital Currencies (CBDCs): The push for Central Bank Digital Currencies (CBDCs) designed specifically to resist AI infiltration could accelerate.
This isn’t sci-fi; it’s a rapidly approaching frontier. Understanding these potential challenges is crucial for policymakers, technologists, and market participants alike. We need proactive strategies to navigate this complex future.
AI #DecentralizedFinance #DeFi #Cryptocurrency #FinancialStability #MonetaryPolicy #CentralBanks #DigitalAssets #Blockchain #FutureofFinance #Regulation #Innovation #Cybersecurity #DigitalTransformation
⚖️ Ethical and Existential Questions
Problems of Sovereignty of data:
Should wealth accumulation be limited to sentient beings?
Rights of AI Agents: If AI generates value, does it have rights to the output?
Who owns data for creating AI’s capital?
If decentralized and unowned, is it a new class of sovereign economic actor?
Healthcare data breaches now cost $11.07M per incident.
The Core Problem: Many healthcare organizations are building AI capabilities on shaky data foundations. When physicians paste patient information into public AI tools (even when attempting to anonymize it), they’re creating compliance risks that extend far beyond the immediate use case. Most public AI platforms weren’t designed with healthcare’s stringent privacy requirements in mind. What Strong Data Foundations Require: 🔐 Zero-trust architecture with end-to-end encryption 📋 HIPAA-compliant governance and audit trails 🔄 Seamless integration with existing workflows 🚫 Data loss prevention that stops PHI/PII exposure ⚡ Ephemeral processing that prevents unauthorized data persistence The Stakes Are High: HIPAA violations can reach high costs, but the real cost is patient trust. Healthcare organizations must balance innovation with responsibility — enabling AI’s benefits while maintaining strict regulatory compliance.
The Path Forward: Healthcare leaders need secure data gateway solution. MetaAll is developing AGI Health Protector that is built around data routing protocol called HGTP
Enter Alliance member: Science Corporation, led by CEO Max Hodak, former President of Neuralink, with a revolutionary approach that could change everything: a biohybrid neural interface that uses living neurons to grow non-destructively into your brain-tissue.
To be continued…
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