Prospective Case Study 1:
Courts and Tribunals Digital Transformation ⚖️⚛️🚀

While UKRI remains a strong candidate for our prototype, an equally compelling opportunity lies with HM Courts and Tribunals. Through firsthand experience navigating the appeals system, we have seen the damage caused by outdated infrastructure. From special character bugs that corrupt uploaded evidence to the complete absence of AI to guide, organise, or validate case files, the current system is archaic, resulting in delays, inefficiencies, and outright procedural injustices.

The proposal is simple: rather than submitting appeals into the flawed service.gov.uk platform, we create a clean new subdomain — sienna.gov.uk — from which different specialist services can branch. For HM Courts and Tribunals, the prototype would be housed at sienna.gov.uk/HMCTS.

This project would be built through Domain-Driven Design (DDD) — a methodology focused on fully understanding the business or problem domain and creating a solution tailored precisely to it.
As Steve Jobs and Bill Gates famously agreed, the reason they succeeded so profoundly was because "they were creating software for themselves."

As a litigant, a technologist specialising in CMS administrative infrastructure, and the designer of the TLS-W🏹 Total Legal System – AI Litigation Weapon, I bring a rare combination of experience to this task. The TLS-W🏹 is, in essence, the system we are using — the design is already there; we are simply applying it to a different problem domain.

Imagine instead of filling out forms uploading chaotic piles of PDFs, appellants are given their own living digital space — a fully structured, intuitive mini-website.
To visualise this, simply look at the SiennaAI.net website you are currently browsing.

On a desktop or laptop, each main dropdown menu represents a core principle or benchmark. Each dropdown submenu represents a key point of significance within that principle, and each side menu links to supporting evidence files. It’s a visually satisfying, easy-to-navigate structure — ideal for judges reviewing on a laptop, juries viewing on a big screen, and fully mobile-compatible for appellants who rely on smartphones rather than computers. The only difference is that on mobile, the menu behaves more like a filing system.

Using TLS-W’s benchmark scoring system, appellants would receive real-time point feedback as they present their case. High point totals would indicate well-prepared, coherent submissions; low scores would signal where additional evidence or explanation is needed, or that the case is not strong enough and one should not waste one's time.

This benefits the appellant by improving their case — and it benefits administrators and judges by streamlining review and validation. In each and every case before assessing administrators and judges will be able to see the score given by the AI system, for each of the benchmarks of the case . Without doubt, in every case, justice will be better served.

Today, judges, clerks, and tribunal staff must manually sort, understand, and evaluate disorganised evidence — often hidden inside broken uploads or fragmented across many files. Our solution presents everything in a clear, navigable format, designed both for human judges and AI pre-validation. Instead of 300 pages of random documents, a judge would explore a structured, logical website, seeing the flow of argument and evidence at a glance.
While some might worry that this could encourage a surge in appeals, our integrated validation and scoring systems would actually help manage caseloads by strengthening good cases and identifying weak ones early. It is not about flooding the courts — it is about bringing fairness, clarity, and 21st-century technology to a system still visibly suffering from software that was literally built in the last century, and even then was not built well.

The CMS 📂💻 Filing System: Why It’s Central to Everything

Creating the CMS 📂💻 — a content management system displayed as a website, as described above — is similar to organising an email account: folders for major subjects, subfolders for specific topics, and sub-subfolders for deeper niches.

The output of the CMS can look like both: a traditional website navigation or a structured document filing system. In fact, if you look at the navigation on the mobile version of SiennaAI.net, you’ll see exactly that — a beautifully organised hierarchy of menus and submenus.
Users would be able to choose their preferred filing style: standard dropdown menus, folder-based layouts, or a combination. Uploading evidence, data, or products becomes intuitive and deeply structured. Everything is designed so that as the user builds their material, AI assists — suggesting where each item should be filed, identifying primary sections, proposing cross-links where needed.

This is not just for courts and tribunals. It’s the same 6 Module Design we would use across sectors — from rentals to healthcare, litigation, and government communication platforms.

In every case, the CMS filing system is the heart. In courts and tribunals, the focus would be on evidence structuring. In healthcare, it would focus more on AI-guided triage and diagnosis. In litigation, it would score and benchmark legal cases, transforming how justice is prepared and delivered.
This intense focus on filing systems comes directly from the business engine behind Sienna AI: the Swapping Menus Function. In the commercial model, users can plug menus — entire sets of products, services, or evidence collections — into each other’s systems, earning referral percentages based on activity. Without perfectly structured filing systems, none of that would work. At ⚛️Sienna AI⚛️ Filing is not just administrative — it’s our economic infrastructure.

Right now on SiennaAI.net, we’re using an S-Web 5.2 structure (old PHP) and beginning to prototype the new CMS systems in React. As soon as Microsoft start up Azure credits are secured, we'll dynamically integrate GPT-4 and Copilot for backend filing and suggestions.

From 🏛️📜Appeals to DWP Scammer Detection🕵️, the CPS, and the Police

🚀 S-Web 6 VC AI CMS’s potential extends far beyond individual appeals — it could transform the entire Courts and Tribunals Service, and with it, the UK’s broader legal and law enforcement ecosystem.

Take the Crown Prosecution Service (CPS) and the police. Today, disorganised evidence management wastes countless hours and causes significant delays. In November 2022, while completing a seven-page police form, I experienced this firsthand — the system crashed on page six after hours of work. This is another example of GDS Government Digital Service CMS inefficiency.

Designed for intuitive organisation, deploying S-Web 6 VC CMS structures documentation for fast retrieval, and AI integration — agencies like the CPS and police forces could dramatically accelerate casework, enhance public interaction, and prepare for systems like Palantir’s advanced analytical platforms, which famously helped locate Osama Bin Laden — proving how structured, machine-readable data can change outcomes.

A modern CMS would benefit every layer of the justice system: criminal trials, civil cases, administrative appeals, and law enforcement evidence handling.

Of the courts' workload, 55% is dedicated to criminal cases, 20% to civil cases, 10% to DWP and welfare appeals, 10% to alternate tribunals, and 5% to other specialist areas.
Despite the dedication of court staff, the UK’s justice system struggles under outdated technology. As of 2024, over 73,000 criminal cases are waiting for trial in the Crown Courts and nearly 63,000 immigration and asylum appeals are pending. Without radical improvement, these backlogs will only grow.

DWP Scammer Detection 🕵️: Some may argue that making DWP appeals easier could increase costs. However, as early as 2011, our proposal for psychology-based CRM detection tools within the Facebook Travel design included ways to ask innocuous-seeming questions that illicit revealing truths about the respondent.

Originally designed for human resources, and now refined through extensive study of behavioural science, we are confident we could build a system that flags and pursues fraudulent appeals — balancing accessibility with integrity.

This approach would support genuine claimants while helping detect inconsistencies that point to potential fraud — ensuring fairness without spiraling costs.

In economic terms, improvements to the appeals system would likely have a neutral net effect — but the gains in public safety, trust, and judicial efficiency would be immense.
Moreover, strengthening police and CPS administration and improving the management of civil and criminal cases would reduce systemic frustration, prevent backlogs, and ultimately help prevent unrest — reinforcing the stability of the social fabric itself.

🏛️🛡️ HMRC Gatekeeper — The AI Assistant Completing Corporation Tax Returns Where No Agent Dares to Tread

This case study can be considered a bonus example — it does not directly address CMS design, but instead demonstrates the power of adapting the GP-AI Gatekeeper concept for HMRC operations. The relevance is clear: GP-AI Gatekeeper is built atop the same S-Web 6 VC AI CMS architecture, and this example showcases how Gatekeeper-style logic can accomplish tasks even HMRC staff cannot consistently handle — and with no additional human training required.

The case in question is discussed in full detail here: 🏛️💻🏛️ HMRC: Autofill Bug Problem from 2017 Not Fixed in 2025 – 🕵️ Is the Service Being Retired Because of This Bug?. Although HMRC is reportedly retiring its corporate tax CMS in 2026, this remains a live opportunity: the same Gatekeeper logic could be adapted across income tax, self-assessment, VAT, and beyond — dramatically simplifying the process for citizens while reducing excuses for non-compliance.

GP-AI Gatekeeper was originally designed to replace receptionists and assist doctors by using a points-based hierarchical logic tree to guide conversations and extract vital information. Applied to HMRC, the same structure would interact with users as they complete tax returns, gently guiding them through each section and ensuring that no crucial details are missed — all without overwhelming technical language or bureaucratic confusion.

While still rough, this 5-hour video documents my own experience navigating the Corporation Tax return process — a task so complex that even senior HMRC support staff could offer little assistance. In two previous one-hour sessions with Adam and Jess from HMRC support, both confirmed that if an AI-based assistant could solve these user challenges, it would be "amazing." It is not only possible — it is already achievable today with GPT-4, and even more so with a dedicated Gatekeeper adaptation.

Economically, the logic is simple: making tax returns easier increases compliance and revenue. Every other investment the UK makes depends on getting this first principle right.

On 🎙️ ☆DF96h5a – UKRI Only Disrupt Low HMRC Tax Companies, this idea emerged: If HMRC were to implement a GPT-4-based assistant — such as the GP-AI Gatekeeper adapted to tax — not only would users receive live, intelligent guidance during their filing process, but every interaction would be recorded. These help chats, in turn, could serve a powerful secondary function: fraud detection.

Two key outcomes follow:

• 1️⃣ If a user is correctly advised and still inputs fraudulent data, the conversation proves intent.
• 2️⃣ The system could be trained to scan conversations for suspicious patterns in real time — identifying fraud before it reaches the submission stage.

This transforms what was once a passive help service into an active layer of fraud prevention — without punishing honest users. As Nick put it: “If you go to HMRC with the intention to commit fraud, I’m not going to make your life any easier.”

It’s a small section in scope, but monumental in implications: making taxes easier + fraud harder = more compliance, less bureaucracy, and increased national revenue. All through one scalable GPT-based logic system.

⚛️Sienna AI Gatekeeper🛡️ Is The 'VC' (Voice Command) AI in S-Web 6 VC AI CMS

⚛️📂 S-Web 6 VC AI CMS – A Stronger UKRI Validation Process ✨

UKRI - UK Research and Innovation

The story of what S-Web 6 VC AI CMS could achieve for UKRI was first explored in 🚀 S-Web 6 VC AI CMS – A Much Stronger UKRI Validation Process.

The original investigation 🕵️ begins by highlighting how our application could have been written by AI — in a way that appeared technically feasible, but wasn’t. Yet without the ability to add links or supporting detail, there was no way for judges to tell the difference between a highly achievable outcome and AI-generated nonsense. The article continues to expand on this issue before arriving at the proposed solution: S-Web 6 VC AI CMS – A Much Stronger UKRI Validation Process.

The S-Web Story is told in four segments:

❌ The Critical Failures of the Current UKRI CMS ❌

Read Complete Article.

The current UKRI/Innovate UK CMS locks applicants into rigid, text-only forms — has no dynamic content, videos or AI assistance. There’s no way to validate ideas, collaborate or build connections between teams and submissions. Even working links are unsupported, so no one can prove what they claim.
This isn’t just outdated — it’s software literally built in the last century, as shown in The GDS Gov.uk CMS Problem. There’s no capacity for AI-powered validation process, no ability to track goals or showcase results.

S-Web 6 VC AI CMS fixes this by turning each application into a compact, fully structured webpage — complete with video introductions, linked references, and optional bonus content.

Instead of judges printing out basic CMS forms, they get a single, navigable page with every answer in one place — easier to read, easier to evaluate, and ready for AI pre-assessment.
While still a work in progress, the live example shows exactly what's possible: siennaai.net/GP-AI-Gatekeeper.php.

🚀S-Web: A 23 Year Pedigree in CMS Design📂

Read Complete Article.

S-Web began in 2002 with CapeVillas.com (S-Web 1 – Desktop only), a CMS that let every team member upload content, add photographs, and manage listings in real time — features Innovate UK’s CMS still lacks. In 2006, we built an affiliate booking system connecting villa owners with global travel companies. In 2010, we attempted full project duplication with S-Web 2 — but poor bug reporting led to its abandonment and a critical lesson learned.
In 2013, we created S-Web 3, integrating old databases with a new point-based CMS logic system, where features were scored — for example, seven pool types from plunge pool (20 points) to spectacular-length pool (200 points). This logic grew into S-Web 4, introducing the ‘My Favourites, My Website’ tool, designed to influence group decision-making. We added APIs to retrieve live availability and pricing, built the S-Web CDS Content Delivery System, the TBS-CC Company Controller, and evolved our scoring logic from the NickRayBot to the SiennaBot IA AI, culminating in the Nudge CRM AI and UCS Hawthorne OKRs, blending perfect information with behavioural science.

• S-Web Development (2015–2016): The S-World Algorithms – Part 2
• The Villa Secrets Secret – 2017 Development Roadmap: Network.VillaSecrets.com

In 2019, S-Web 5 successfully demoed the principle behind the Swapping Menus Function by adding an Experience Africa menu to Cape Villas.
Without lifting a finger, Cape Villas earned $10,000 when a client booked a safari — passive income generated simply by hosting a menu. It was a powerful validation of the Villa Secrets Franchise Model.

By 2021, the S-Web 5.1 Row Widget CMS enabled Lux Guides.com to build a homepage in just 51 seconds, launching alongside updated sites for Cape Villas, Experience Africa, Villa Secrets, Luxury Safari, and Villas Café. This marked the start of S-Web’s challenge to WordPress — claiming up to 60,000x faster growth potential via interconnected networks.
In 2022, S-Web 5.2 was repurposed for generic use cases such as personal blogs, grant submissions, and investor pitches. The Sienna AI site you’re using now is built on S-Web 5.2. But to deliver features like the Swapping Menus Function, voice command, app generation, and full AI integration — in early 2023, we began designing S-Web 6: a new CMS built on everything we’ve learned since 2002.

🚀S-Web 6: The Next Evolution in AI-Powered CMS⚛️📂

Read Complete Article.

S-Web 6 is not just a CMS upgrade — it’s a global disruptor in website and app creation. On one front, it challenges WordPress for its 40% share of the global website market. On the other, it redefines digital infrastructure as the foundation for Economic AI. The future of digital infrastructure isn’t built on static websites — it’s built on systems that think.

Similarly, the future of search won’t be dictated solely by external engines; what the AI knows and where it sources its answers from is becoming more important than traditional rankings.
In this landscape, AI built directly into websites becomes the key to discoverability. It’s no longer about search engine optimisation — it’s about language model optimisation, embedded directly into the CMS.

That’s why S-Web 6 is integrating AI at the heart of the CMS Mothership 🛰️🛸🛰️ — transforming every site into a living, evolving ecosystem. Comments, reviews, and activity dynamically trigger updates. Large language models like GPT-4 act not as tools, but as embedded operating systems — wired to microservices, APIs, and live content feedback loops. With Voice Command CMS functionality, S-Web 6 becomes self-updating: pages rewritten by AI with the credibility and freshness of a news site.

Take UKRI as a use case: every competition entry becomes a dynamic website — useful for commercial growth and collaboration. In each case, AI can pre-assess competition entries, pre-scoring each page on its ingenuity, originality, usefulness, and potential gain in terms of increasing UK GDP or other metrics based on competition objectives.

Post-competition, these sites become ongoing innovation hubs. Comment sections invite public and peer engagement. AI evaluates the feedback, updates project pages, and even helps co-author refinements.

Entries aren’t judged once — they evolve continuously as UKRI gains real-time, post-competition tracking data: awarding future bonus points to past winners who succeed — and identifying companies whose ideas didn’t live up to expectations, excluding serial offenders from the process.

And thanks to the Swapping Menus Function, project pages can earn passive income, link to complementary technologies, or share resources — creating decentralised innovation networks that grow stronger together. One project’s progress becomes another’s solution. The entire ecosystem becomes collaborative, compounding, and economically self-sustaining.

But this isn’t just a tech upgrade. S-Web 6 VC AI CMS is the product of 14 years of macroeconomic AI design — entangled with foundational projects like S-World.biz (2011), American Butterfly (2012–13), Network.VillaSecrets (2014–17), Angel Theory (2016–20), S-RES Financial Engineering (2012–21), The S-World Algorithms (2012–22), and the Sienna AI UK Butterfly Project Podcast 🎙️ (2024)

The Sienna AI

Economic AI Foundation

From T1 🚀 S-Web 6 VC AI CMS to the PQS, M-Systems and The 10 Technologies (T10T)

Ask today’s leading AIs — GPT-4o, Gemini, Copilot — what economic or macroeconomic AI is. You’ll get vague summaries about using AI to model GDP, inflation, unemployment, and economic behaviour — but no cohesive theory, no defined discipline. GPT-4o even concedes the field barely exists at all.

📄 Click here to see all six replies.
What follows isn’t a rebuttal — it’s a foundation. A clear proposal for what this field must become.

1. Economic AI involves simulating future economic states, and copiloting humanity and the planet toward those with maximum utility and stability. It’s about shaping — not predicting — the future.
2. A core component in many simulations is Š🌀ŔÉŚ — Šavings + Ŕevenue × network Éfficiency × Śpin.

Where others attempt to forecast the future, since 2011 Sienna AI has aimed to shape it. Even before fully understanding AI, we developed the 87 Quintillion Histories model to simulate a vast array of potential futures — and identify the path of optimal outcomes. This is Macroeconomic AI: a system designed to guide the planet toward A More Creative Capitalism — shaping, not predicting, the future.

The backstory of Š🌀ŔÉŚ and The Ten Technologies — T10T begins in November 2011 at S-World.biz, with the altruistic concept of the Ecological Experience Economy (EEE) and its early simulations of Cities of Science. A year later, in the US macroeconomic blueprint American Butterfly.org, Book 2, the logic was entangled with String Theory and government efficiency modelling (Sienna.gov). This vision evolved into The PQS: Predictive Quantum Software.

Its “quantum” designation was inspired by the Monte Carlo N-Particle (MCNP) transport code, originally developed during the Manhattan Project to simulate complex nuclear processes. Drawing from this foundation, PQS incorporated similar probabilistic simulation techniques to model economic systems — leading to the conceptualisation of the Monte Carlo Quantum Probability Simulator (MCQPS).

Remarkably, a near-identical structure — Monte Carlo Tree Search (MCTS) — was later adopted by DeepMind as the strategic core of AlphaGo in 2016. While our implementation was never quantum computing in the strictest sense, the underlying logic — simulating decision paths through branching probability — mirrored the method that helped build one of the most powerful AIs in history, four years later.

By 2016 — the same year DeepMind’s AlphaGo stunned the world by defeating the Go world champion using Monte Carlo Tree Search (MCTS) — I was deep into designing and building S-Web 3 and its commercial software. At the same time, my passion for modelling business and economic systems through quantum theory inspirations (2), (3) revealed a circular economic logic built around the Predictive Quantum Software. Beginning with S-Web, this model spiralled clockwise through top-tier business algorithms, flowed through POP, and descended into virtual social and business systems, continuing into QuESC Mission Control.

Next, the loop returns clockwise from bottom right to top left.
QuESC (the Quantum Economic System Core) funnels into S-World UCS (Universal Colonisation Simulator) Game, which branches into two outputs. One generates its advanced simulation layer — UCS Voyagers — while the other powers the macroeconomic Angel City Simulations. Together, these complete the full circle of predictive planning, capital formation, and reinvestment — as new ventures in new cities begin again using S-Web CMS and the TBS – Total Business Systems. The journey resets for a new generation of users.

By 2017, the vision evolved into a full-fledged cosmology of economic logic: M-Systems — An Economic Theory of Everything. Starting with the S-Web CMS Framework, it passed through the TBS algorithms. But this time, after POP, we encounter Paul Romer’s Charter City concepts — The Theory of Every Business (Large Resort Developments and Industry). From there, it flows into the Virtual Networks, followed by S-World Film (2, 3) — before arriving at the string theory–inspired business logic of POP Super Coupling.

After Mars Resort One (2017), Š🌀ŔÉŚ returned as M-System 10, followed by QuESC, UCS, and the UCS Voyagers. These culminated in the multi-layered Angel City infrastructure — concluding with the mysterious, still undefined M-System 16: Angelverses — the economic operating system that completes the circuit and returns to M-System 1.

In 2018, after diving deep into economics, I began condensing the altruistic and theoretical physics–influenced components of the macroeconomic model into the book A More Creative Capitalism — the name inspired by a line in Bill Gates’s 2007 Harvard commencement speech.

In the second half of the year, I began simulating Š🌀ŔÉŚ — culminating in December with the Malawi History 2 Simulation. The world’s lowest-GDP country was used as a hard target to test the system’s potential. This groundwork modelled Malawi’s journey from almost Zero to One percent of global GDP between 2024 and 2051.

In 2019, the M-Systems diagrams were updated twice. First, Behavioural Economic Systems were linked to the S-World Film component. Then came an unexpected influence: Cosimo Yap’s LitRPG novel Sacrificial Pieces (Book 3 of The Gam3 series), introduced a powerful AGI named AngelWing, which redefined the final node in our framework — transforming the label from “Angelverses” to “AngelWing” and shifting the focus from string theory to AGI.

AngelWing represented an unknown force — a future AI so advanced it could orchestrate the spin cycle of the macroeconomic system. It was a metaphor waiting for technology to catch up.
We called this new graphic S-World AngelWing — The M-Systems Economic Software Framework:

In 2019, with A More Creative Capitalism as a launchpad, I outlined a three-part book series conceptualised as The What, The How & The Why:

📚 The Three-Book Framework: The What, The How, and The Why

• ID: 1026) 📖 1+2+3: The What, The How, The Why — June–Nov 2019
• ID: 1038) 📖 S-World Stories — A Graphical Presentation (Aug 2019)
• ID: 1071) 📖 The Network and Book 1: THE WHAT (Nov 2019)

1. Book 1 — The What (What we had been creating since 2000) — We had been working on this since the year 2000, producing The Villa Secrets' Secret in 2017 (🌐), including: S-Web, the TBS, Network Strategy, Mandate Strategy, S-Web CDS, the Nudge-CRM-AI, the TFS, the Company Controller, and UCS Hawthorne.
2. Book 2 — The How (How Š🌀ŔÉŚ turns software into macroeconomics) — Focused on Š🌀ŔÉŚ financial engineering and its application through simulations like Malawi History 2, followed by the Malawi History 3 simulations exploring how network-based economics could help Malawi grow from zero to one percent of global GDP.
3. Book 3 — The Why (Why this work matters) — Explored altruistic and philanthropic special projects and goals, drawing from our earlier 2018 work: Ripple Effects and Elephants for Paul G. Allen.

With Book 1 complete and the Š🌀ŔÉŚ simulations of Book 2 well underway, I turned my attention in late 2019 to Book 3 — The Why. This volume would become the ethical and humanitarian cornerstone of the entire system — exploring why we were building these technologies, and for whom.

Book 2 — The Why

64 Reasons Why

From early visions like “African Rain”, a geoengineering initiative to reforest the Sahara; to “The Yellowstone Lid”, a protective structure designed to contain the supervolcano threat; and “Mission Gliese”, a speculative roadmap for interstellar colonisation — since 2012, Special Projects had been woven into the logic of American Butterfly and S-World UCS – Universal Colonisation Simulator.
Originally, the goal was economic longevity: to design projects with such enduring utility they could yield near-perpetual returns — like engineering a “never-ending bridge.” But from 2015 to 2018, documented across AngelTheory.org, the lens shifted. By the time we reached A More Creative Capitalism the simple logic that Grand Śpin Networks (Cities) in locations in abject poverty were Special Projects, evolved the objective from imaginative theory into practical economic strategy.

By 2019, over 40 such initiatives had been outlined. Expanding this to 64 special projects was a natural next step. The number was chosen deliberately: 64 is 8 cubed — a recursive nod to POP’s chaos-informed logic, and a symbol of balance between abstract theory and economic purpose.

During the Š🌀ŔÉŚ Malawi History 3 simulations — which modelled a trajectory from 2024 to 2080 — capital allocations were assigned to each of the 64 special projects, transforming vision into testable economic design. This process gave rise to the principle of Tax Symmetry.

That journey began in 2017 with the Mars Resort 1 thought experiment — a testbed for self-sustaining economic colonies:

By the end of that year, Mars Resort 1 simulations had demonstrated how Š🌀ŔÉŚ could function inside self-taxing colonies. In 2018, Malawi — with almost zero GDP — served as a grounded analogue. In such an environment, traditional taxation was eclipsed by the network’s ability to deliver meaningful economic output.

The logic was simple: the government outlines what it wants — education, healthcare, infrastructure, and 61 other priorities — and the network sets out to deliver them. Not by paying tax in currency, but by delivering outputs that fulfil those needs. In essence: the network pays tax in deliverables — where public goods are generated through entrepreneurial infrastructure, aligned to decentralised public-private partnerships.

Inspired on the one side by Paul Roma's Growth Theory and on the other by Donella Meadows’ Thinking in Systems philosophy, each project was designed to be net-zero and regenerative. 64 Reasons Why then became the sister doctrine to Š🌀ŔÉŚ — laying the blueprint for net-zero cities and resilient infrastructure throughout the developing world. And by its very design, this system didn’t just address economic migration — it could eliminate climate collapse and resource scarcity. In terms of philanthropy and effective altruism (EA) — 64 Reasons Why ticks all the boxes

• ID:717) 🌐Ripple Effects and Elephants January 2018
• ID:950) 📄Special Project 33 — Growth Theory versus Climate Change 24th Nov 2018
• ID:929) 📄46 Reasons Why — Growth Theory Externalities — The-City 7th Nov to Dec 2018
• ID:1054) 📄Š🌀ŔÉŚ & 64 Reasons Why — Antitrust Oct 2019
• ID:1050) 📖Sixty-Four Reasons Why — Summary September 2019 to March 2020 [See Web 🌐 Version]
• ID:991) 📖3. Sixty-Four Reasons Why — Complete Book Feb 2019 to Oct 2020

But given This journey was a journey of technology this really is Effective Accelerationism (e/acc) #Accelerate, #TheOnlyWayOutIsThrough, #MouthsToFeedWorldsToBuild.

Book 2 — The How

S-RES (2012 — 2021)

High-Octane Financial Engineering

Pecunia, si uti scis, ancilla est; si nescis, domina.
(If you know how to use money, money is your slave; if you do not, money is your master)

The Š🌀ŔÉŚ Algorithm

Š is for Šavings – written either: Šavings or just Š
Ŕ is for Ŕevenue (including investment) – written Ŕevenue or just Ŕ
É is for recycle-Éfficiency, which is the percentage of money, say 90%, that is spent by one company in a network with a single central bank to another company in the same network with the same central bank. – written recycle-Éfficiency or just É
Ś is for a Śpin, the number of times the money is recycled from one network company to another and another in the same year. – written Śpin or just Ś

In 2020, with 64 Reasons Why complete, we focused on Book Two, which included extensive simulations dedicated to Š🌀ŔÉŚ. In Malawi history two we had simulated how S🌀RES could take Malawi from zero to 1% of global GDP between 2024 and 2051, But this included a trade component that whilst reasonable could certainly be argued so in Malawi history 3 we took away the trade and built the model based purely on investment in the City, Many simulations were created. The Malawi History 3 videos start at number 26 and continue up to 43 on The Sienna AI YouTube Channel S🌀RES playlist

• 1135) 📖2. The How — Š🌀ŔÉŚ & The City — Oct 2020–Feb 2021 ℏ🌪️
• 1152) 📖2. S🌀RES & The City 'Extra' — Nov 2020–Apr 2021 🌪️
• 1171) 📖2. Š🌀ŔÉŚ™ & The City — Part 2 — Jan–Apr 2021 🌪️

Click here for the full version.

In 2024 a network of businesses has $6.32 billion in savings and revenue (Š & Ŕ) of which 90% is spent on goods and services from other businesses or personnel in the same network.

Which at an 'É' (recycle-Éfficiency) of 90% increases the cash flow as follows;
The initial $6.32 + the recyled $5.68 billion = $12 billion.

However for this exercise, for History 3 (the simulation we are analyzing), we report only the pre Śpin income, which when some other items are added and taken away equals $5,685,975,000.

We will see this figure appear as the first entry on the 2024 to 2080 History 3 cash flow statement presented shortly

Now we apply Śpin (Ś) to the 2025 figures . As before instead of spending the money once a year, we spend it twice creating $14.89 billion in cash flow. Plus, critically, $7.10 billion remains at the end of the year, and is transferred to 2026, this is called Šavings (Š) or sometimes The Law of Conservation of Revenue

The following year (2026) Śpin increases to 3, so we spend the money three times in a year creating $26.85 billion.
Note the figures are effected by aditional in and out flows and they wront tally without them. To see the additional 'in and out flows' go to: 11.11__S-RES__BASIC

As part of the Š🌀ŔÉŚ Production, influenced by Peter Theil's Zero to One, a book that finally understood the benefits of network monopolies, A fourth book was added to the series called:

Book 4 — The Future

10x Our Future

A work in progress for Peter Thiel

ID:1098) 📖10x Our Future — Zero To One — 64 Reasons Why — The-Grand-Design March to 28 April 2020

1116) 📖4. 10x Our Future (A work in progress for Peter Thiel) August to September 2020

Although written out of sequence, these works eventually formed a coherent vision — blending macroeconomic simulation, ethics, and systems engineering.

By 2021, a more accessible system was required — leading to the creation of The Ten Technologies, where Technology 10 became the combinatorial explosion of the nine lower technologies — the exponential agent of transformation.
Keeping the circular architecture, the top row begins with Technology 1: S-Web. Then Technology 2: business systems to manage S-Web-generated companies. Technology 3: distribution networks — everything it takes to make a sale. Technology 4: S-World Film, where content becomes king.
On the right-hand side, beneath S-World Film, we find Technology 5: VSN – Virtual Social Networks. Below that, Technology 6: UCS – Everything as a Game. Gamification became simulation — and ultimately, the foundation for our Freehistory Models, inspired by Feynman’s sum over histories and Isaac Asimov’s psychohistory.

On the bottom row we begin to see the core of a Macroeconomic AI — not theory, but an actual system that’s been in development since 2011. Technology 7: S🌀RES Financial Engineering. Technology 8: Net-Zero DCA Soft — with special projects created through Dynamic Comparative Advantage. Technology 9: Grand Spin Networks — evolving from simple city plans to national infrastructures built around S🌀RES and VSN frameworks.
And then there’s AngelWing. The misunderstood Technology 10 — the combinatorial explosion of all nine lower systems when entangled with AI. In 2021, its purpose was still unclear. But by 2023, as GPT-4 began speaking fluently, the mystery resolved. M-System 16 – AngelWing became the large language model itself — the OS that closes the macroeconomic loop and begins a new Śpin of Š-ŔÉŚ.

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Solve Capital, and Use It to Solve Everything Else

There are millions of engineers and organisations who know more about the science of deep learning than we do. In 2010, Demis Hassabis, Shane Legg, and Mustafa Suleyman founded DeepMind. In 2015, OpenAI was born under Elon Musk and Sam Altman — focused on recursive self-improvement and general intelligence. Their goal was the mind — the AI itself.
But ours was the world the AI would live in.
No one was focused on rebuilding the infrastructure of capitalism itself — cities, logistics, and global resource flows — through decentralised logic, virtual design, and real-time economics. No one but Sienna Software.
Our view? The AI doesn’t just model economics — it replaces inefficiency, rewrites logic, and regenerates capital at the core. Read more: 🕵️ UKRI Disruption 💥 Prologue: ⚛️ T10T – Laying the Tracks for Macroeconomic AI.

Often — the solution really is just money.

DeepMind famously said, “Our goal is to solve intelligence, and then use that to solve everything else.”

Our approach since 2018: “Create a more creative capitalism — and then use that to solve everything else.”

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AGI Needs a Prompt

From Supremacy by Parmy Olson:

“Altman had an answer for anyone worried about money because while there was a tiny possibility that AGI might bring about apocalypse, there was a bigger chance it would usher in an economic utopia... He explained OpenAI would capture much of the world’s wealth through AGI and redistribute it... $100 billion... then $1 trillion... then $100 trillion... He admitted he didn’t know how his company would do it. ‘I feel like AGI can help with that,’ he added.”

To Sam Altman, OpenAI, and the effective altruists: If AGI is the mind, this is the map. These are the blueprints for how to equitably distribute wealth, without losing dignity, employment, or purpose.
Start here: $1039 Trillion GDP Simulation — the macroeconomic path built in 2021.

The Economics of AGI

S-Web 6 VC AI CMS

Economic AI Foundation

The 🚀S-Web to Š🌀ŔÉŚ Macroeconomic AI Bridge

Ask today’s leading AIs — GPT-4o, Gemini, Copilot — what economic or macroeconomic AI is. You’ll hear variations of the same thing: the use of AI to analyse, forecast, optimise, and simulate economic trends. GPT-4o admits the field doesn’t really exist yet. Gemini and Copilot give what you’d expect from stitching together “economic” and “AI” — modelling GDP, inflation, unemployment, and financial decision systems.
📄 Click here to see all six replies.

But none of them describe this:

Economic AI uses Š-ŔÉŚ — Šavings + Ŕevenue × network Éfficiency × Śpin — to increase GDP.
In the 10 Technologies framework, AI is the combinatorial explosion of the nine lower technologies.

The backstory of the Ten Technologies begins in November 2011 at S-World.biz, with the altruistic concept of the Ecological Experience Economy (EEE) — a macroeconomic system designed to guide humanity for the next 14 billion years. One year later, this vision evolved into PQS: Predictive Quantum Software — a proposed logic engine for economic simulation built on entangled networks and string theory principles.

Four years later, a deeper understanding of quantum mechanics revealed a circular economic design: starting with S-Web, it spiralled clockwise through top-tier business systems and down into scalable city-building algorithms — completing a loop of predictive planning, capital formation, and reinvestment.

By 2020, that loop had evolved into a full-fledged cosmology of economic logic: Š-ŔÉŚ returned as M-System 10, followed by QuESC, UCS simulations, and the multi-layered “Angel City” infrastructure — culminating in the mysterious and still undefined M-System 16: AngelWing.

In the M-Systems diagrams, AngelWing represented an unknown force — a future AI so advanced it could orchestrate the spin cycle of the macroeconomic system. Named after Cosimo Yap’s AI in The Gam3, it was a metaphor waiting for technology to catch up. At the time, we didn’t know what it was — only that it was essential.

From 2017 to 2021, extensive simulations were dedicated to Š-ŔÉŚ (S🌀RES), its predictive cycles forming the engine room of macroeconomic growth. It became so integral, we’ve since given it a dedicated dropdown menu. Explore that menu to grasp the full power of S🌀RES.

There’s a world of information in “A More Creative Capitalism” (2018), and more recently in How Š-ŔÉŚ™ Generates US$ 1039 Trillion by 2080. But for now, what matters most is understanding the foundation: the first of the nine lower technologies.

Technology One is S-Web VC — the CMS infrastructure accessed via vocal command. This is where it all began. All macroeconomic systems, from M-System 1 through to AngelWing, sit upon this data-management layer.

In 2020, it was time for a simpler, more accessible expression of this design: The Ten Technologies, where the 10th would become the combinatorial explosion of the nine others — the exponential agent of transformation.

What you're looking at on the bottom row is the foundation of a MacroEconomic AI — not in the abstract, not in theory — but something we’ve been building since 2011, long before “AI” became the buzzword.

And so, when GPT-4 finally began to speak clearly in 2023, it shouldn’t have been a surprise to hear that M-System 16 — AngelWing — the economic operating system, which returns macroeconomic cities back to M-System 1 for another Śpin of Š-ŔÉŚ, would turn out to be the communicative power of a large language model AI.

Having spent 2002–2010 developing S-Web 1, from this base we designed eight more interlinked technologies — long before we knew what the missing piece was. That missing piece was AI — the misunderstood 10th technology.

It was a shock to find we’d built the entire foundation for an economic system — and what we lacked was only the final cognitive layer. AI was the closing loop. The combinatorial explosion that turns infrastructure into capital — and capital into progress.

And for anyone looking at the 32x GDP — like it’s a doomsday machine, that will just destroy the world quicker, take a good, hard look at Technology Eight: Net-Zero DCA Soft.

For now, we return to our main subject S-Web 6 VC AI CMS – Stronger UKRI Validation. But do so with the understanding that this isn’t just a new CMS — it is the visible surface of an innovation ecosystem with over a decade of design beneath it.

There are millions of engineers and organisations who know more about the science of deep learning than we do. In 2010, Demis Hassabis, Shane Legg, and Mustafa Suleyman founded DeepMind. In 2015, OpenAI was born under Elon Musk and Sam Altman — focused on general intelligence and recursive self-improvement. Their goal was the mind — the AI itself.

But ours was the world the AI would live in.

No one was focused on how the infrastructure of capitalism itself — cities, logistics, and global resource flows — could be rebuilt using decentralised logic, virtual planning, and real-time economics. No one but Sienna Software.

We only began integrating AI, via Azure, in 2024 — when we realised that what we’d been designing for over a decade was theoretical... until it could be powered by the cognitive breakthroughs made by DeepMind, OpenAI, Meta, and others.
So no — it’s not surprising that AI models today don’t yet understand what Economic AI really is. They define it as a tool to help existing economics work better.

But what we’re saying is something far more radical:

The technology becomes economics. The AI replaces economic inefficiency — not by modelling it, but by rewriting it. We make sustainability possible not by lowering costs, but by building an economy strong enough to afford what matters.

See: 🕵️ UKRI Disruption 💥 Prologue: ⚛️ T10T – Laying the Tracks for Macroeconomic AI

📚 The Four-Book Framework: The What, The How, The Why, and The Future

Across 2018–2021, eight key publications helped shape the macroeconomic foundation that would become T10T. The framework began with four core books, conceptualised as The What (our software inventions), The How (economic systems and city models), The Why (purpose and responsibility), and The Future (our response to Zero to One by Peter Thiel).

• 0844) 📖1. A More Creative Capitalism (Complete) — Oct 2018 ℏ🌪️
• 1135) 📖2. The How — Š🌀ŔÉŚ & The City — Oct 2020–Feb 2021 ℏ🌪️
• 0991) 📖3. Sixty-Four Reasons Why — Nov 2019 ℏ🌪️
• 1116) 📖4. 10x Our Future (For Peter Thiel) — Sep 2020 ℏ🌪️

Extended Reading & Technical Addendums:

• 1026) 📖1+2+3: The What, The How, The Why — June–Nov 2019 ℏ🌪️
• 1152) 📖2. S🌀RES & The City 'Extra' — Nov 2020–Apr 2021 🌪️
• 1171) 📖2. Š🌀ŔÉŚ™ & The City — Part 2 — Jan–Apr 2021 🌪️

That’s how you solve Net-Zero cement.

Standard cement is three times cheaper than its net-zero alternative. Most governments — even when they know this — keep buying the cheap version. Instead of giving up and choosing what’s unsustainable, we raise the capital required to choose better. That’s the goal of A More Creative Capitalism — to afford what matters.
And for those on the other side of the aisle: see Š🌀ŔÉŚ & The City — that’s how you reverse economic migration. By building vibrant, local economies, powered by meaningful employment, you make it so people no longer need to leave home to survive. That’s how you stabilise GDP while solving climate collapse.

Often — the solution really is just money.

DeepMind says: solve intelligence and it will solve everything else. And we agree — eventually. But until AGI arrives — and while Sam Altman openly admits there’s no economic strategy yet for that promised infinite wealth — we believe something else is needed.
Between 2017 and 2022, I wrote and recorded over 2,000 pages exploring this different angle. Most nights I would dream in layers — simulations, theories, LQG, cities — entire operating systems in my sleep. I haven’t yet gone back to fully trace the capital flows described in these books. But from personal, immersive experience: the economic foundation is already there. Hidden in plain sight.

Where DeepMind solves the mind...

We solve the money. 🌀

Fix capital. And everything else begins to fix itself.

A Unified Vision for UK Innovation with ⚛️Sienna AI⚛️

Right now, UKRI's innovation framework is fragmented, inefficient, and failing to realise its full potential. Its £8 billion annual budget is scattered across hundreds of disconnected projects, each treated in isolation, never truly contributing to something greater than the sum of its parts.

But what if it did?
What if, instead of funding a collection of disjointed experiments, UKRI operated like a master architect—curating, connecting, and entangling every funded project into one singular, world-changing vision?
That’s what Sienna AI and S-Web 6 can enable.

From Individual Projects to a Unified Ecosystem

Take medical AI. A while ago, I spoke with a multi-competition-winning company developing AI-powered scanning technologies. Their work was impressive — but it was just one of many teams worldwide doing the exact same thing.
Will their work lead to a breakthrough? Maybe. Will it be patentable? Probably not. Will it be remembered in five years? Unlikely.
But now imagine if all of these grant-winning medical AI projects — every fragmented piece of UKRI-funded research — were brought together into something truly revolutionary.
Imagine if the technology developed in these projects wasn’t just buried in academic PDFs, but actively deployed into a national, AI-powered healthcare system.
Consider Stage 16 of GP-AI Gatekeeper — a “future” concept where patients receive their scan results before they’ve even stood up. Not in a decade. This year.
The scan itself takes seconds. The data can instantly be fed into LLMs — potentially trained on models UKRI already funded. The results can be interpreted in real time. The only thing stopping this is the lack of integration, ownership, and vision.
Right now, it can’t happen. Because UKRI doesn’t own the technology it funds. It doesn’t unify its investments. It doesn’t think in ecosystems.
And that’s not a missed opportunity — that’s a fundamental failure in national innovation strategy.

The Problem is the System Itself

With Sienna AI (S-Web 6), the UKRI ecosystem could be transformed. Not with a basic CMS we outperformed in 2002 — but with a platform built for collaboration, continuity, and AI-driven growth.

• Instead of 100s of isolated competitions, UKRI would execute on a unified vision for the future.
• Projects would be linked, tracked, and accessible — not just to administrators, but to other researchers and AI systems.
• Innovation would accelerate by compounding on existing breakthroughs — not restarting from scratch.

This isn't just about healthcare — it applies to every sector UKRI touches.
And yet, there’s still no searchable database of funded researchers. No way to query who can code LLM integrations. No way to find someone who can replicate foundational models like DeepSeek or work with OpenAI APIs. Despite funding thousands of projects, UKRI still lacks a discoverable talent network.
Why? Because the system is designed to silo. Whether by oversight or by design, it ensures that the same groups win repeatedly — with little collaboration, minimal accountability, and no path to large-scale transformation.

The UK Needs a Real Innovation Ecosystem

With Sienna AI and the OKR system, the UK could do what no country has done before: build an AI-first, collaboration-driven, fully integrated innovation ecosystem.
One that doesn't just fund ideas — it grows them. One that doesn't just publish outcomes — it tracks them. One that doesn’t leave results to chance — it guides them through data and design.

This would be a global export product — not just for the UK, but for every innovation economy.
Because a system like this doesn’t exist anywhere else.
With Sienna AI, UKRI wouldn’t just fund projects — it would fund the future.
This isn’t about better grant forms. This is about building the ecosystem that makes world-changing breakthroughs inevitable.
This is how the UK stops lagging behind.
This is how the UK leads.

🚀 The question isn’t whether UKRI should do this.
🔑 The question is: how long will they wait before they realise they must?

⚛️📂 S-Web 6 VC AI CMS – Beyond A Stronger UKRI Validation Process ✨ https://siennaai.net/6M/The-Making-of--UKRI-Disruption-vs-T10T-Keynes-Multiplier.php https://siennaai.net/6M/UKRI-AI-Grant-Assessment-System.php https://siennaai.net/6M/GOV-COMMs-OKRs.php

Thank's for reading :)

Sienna 4o