At this point, we shift from government software critique to what we enjoy most — and what ⚛️Sienna AI⚛️ is ultimately about: Sienna.gov and The Economics of AGI.
Below is our signature graphic — the latest design in the Sienna AI series — showing how S-Web 6 became Technology One of a much larger framework that has evolved steadily since the year 2000. This is T10T: The Ten Technologies of Sienna AI.
To the pioneers of artificial intelligence — many of whom are building AGI under the assumption that it will eventually solve the world’s social and economic problems — we offer this: DeepMind says, “Solve intelligence — and let that solve everything else.” But no one yet knows how AGI will operate at the political, economic, or social level. There is no roadmap.
We’ve been working on exactly that roadmap since 2011. It remains the only comprehensive macroeconomic framework for AGI in existence. Where others focused on the mind, we focused on the world the mind would live in. Where others tried to solve intelligence, we tried to solve capital. T10T by Sienna AI is a responsible growth engine — and of the Ten Technologies, only the tenth is AI. The other nine build the real-world infrastructure that intelligence will need if it’s to benefit humanity at scale.
In Part 2 we focus on laying the tracks for macro-economic AI by building the first Sienna.gov pilots.
Our first proving-ground is UKRI. We’ll start with the
AI Grant Assessment System (catching polished-but-empty
Grantify bids), plug it into the GOV-COMMS OKRs human-expert
network, and—once the pipes are clear—show how high-impact ideas like
GP-AI Gatekeeper (🎯 £112-£147 billion annual uplift) can flow
through the system. Finally, we contrast this cooperative model with
UKRI’s current “disruption” approach—highlighting why 45 000 isolated
projects can’t compete with one interoperable T10T framework.
AI-Written Grant Applications Problem
🕵️ Solving the UKRI AI-Written Grant Applications Problem
UKRI’s grant pipeline is the ideal test-bed for a first Sienna.gov pilot.
By filtering out polished but hollow “Grantify” bids, surfacing genuine
breakthroughs, and routing them to the right human experts, we unlock two
immediate wins:
• sharp administrative savings inside an £8 billion funding engine, and
• a clear runway for high-impact ideas such as GP-AI Gatekeeper — a
£112-£147 billion annual boost already on UKRI’s desk.
While grants are our showcase, the same
AI-vs-AI pair-wise comparison method scales far beyond innovation
funding. Think exam boards vetting AI-assisted A-levels, universities
screening PhD theses, or recruiters ranking technical take-home tests.
Instead of banning AI (an arms-race we’ll lose), we combine human judgement
with an “AI assessor” that asks one simple question:
“Given two submissions, which truly demonstrates deeper insight and
skill?” The result is fairer, faster evaluation in any domain where AI
support is now the norm — education, accreditation, even hiring.
⚔️ The Real-World Problem With AI-Generated Grant Applications
This line of questioning — how AI-generated grant applications can mislead even experienced reviewers — first emerged during our work on ⚕️⚛️GP-AI Gatekeeper 2025.
Stage 10 of that 16-stage design addressed what we believe is the NHS’s most critical structural problem:
Stage 10. Eliminate Medical Record Fraud
Addresses the NHS's biggest systemic issue by ensuring accurate records, reducing cascading misdiagnoses.
During testing, GPT-4o — referencing only the summarised version above — ad-libbed a polished solution: "The GP-AI Gatekeeper incorporates mechanisms to detect and flag inconsistencies or fraudulent activities within medical records. By ensuring the authenticity of patient data, healthcare providers can make informed decisions, and trust in the healthcare system is bolstered."
On paper, it sounded credible. But in reality, it was completely unworkable. NHS medical records are fragmented across multiple health trusts, inconsistent, and often span over 500 pages per patient. They are not easily converted into AI-readable formats. And any automated analysis is fraught with legal and privacy concerns — the same issues that derailed DeepMind’s £250M NHS partnership, despite being led by co-founder Mustafa Suleyman himself.
By contrast, the actual Sienna AI solution is simple and scalable. GPT-4o acts as the receptionist, recording the patient’s spoken account, reading it back for verification, and submitting the memo to the GP. That memo becomes part of the record — in the patient’s own words. This single feature eliminates a major source of fraud: the deliberate omission of negligence-related data by clinicians seeking to avoid GMC scrutiny or litigation.
This design didn’t come from AI alone. It came from the partnership between a human with real-world experience and a language model with technical fluency. That’s how Sienna AI operates: human + AI, always. But the lesson here is critical — left alone, AI can produce ideas that sound impressive but collapse under practical scrutiny.
And this is personal. Due to a cascade of medical record fraud — culminating in a dangerously misprescribed nerve agent (Lyrica/Pregabalin) — our founder has been denied appropriate neurological care for over three years. Only last week we found out every specialist involved, from GP to surgeon, failed to document the truth.
This isn’t an isolated case; it's part of a wider culture of omission dating back to early 2000s mass tort legal reform. Our ongoing legal case now underpins the development of TLS-W🏹 — our AI litigation weapon. We believe the scale of medical record fraud in the UK is extreme — and one of the largest hidden contributors to NHS waiting lists.
Our 16-module Gatekeeper system was forecast to deliver between £112B–£147B per year in productivity and public savings. That is exactly the kind of innovation UKRI should be funding.
And yet, in a cruel twist, our application — co-authored by human and AI — was disqualified due to formatting limitations in the outdated CMS still used by Innovate UK. The irony: the very digital infrastructure we’re seeking to replace was responsible for blocking one of the only solutions that could fix the system itself.
Meanwhile, purely AI-written entries — built by services like Grantify — are flooding the system with polished but often hollow applications. With no scalable way to tell the difference, Innovate UK appears overwhelmed.
This story became the foundation of our applied research, which we’ve documented in detail here:
by Nick Ray Ball and Sienna 4o🛰️👾 (The “Special One”)
3 May 2025
This page is part of our GDS (Government Digital Service) GOV.UK CMS Problem/Solution series and offers more than just a cautionary tale. It provides a research-backed approach to AI-assisted grant validation. Our tests show that when GPT-4 is given complex scoring instructions for a single submission, the results are inconsistent. But when it’s shown two entries and asked a simple question — “which is better?” — the answers become clear, reliable, and actionable.
This method isn’t just useful for spotting AI-generated content. It’s useful full stop. Humans can write nonsense, too — and this system catches both. We’re not offering software. We’re offering insight. This technique can be replicated by UKRI or any agency without needing to license anything from us. Just apply the comparative model. That alone will dramatically improve grant evaluation fairness and impact.
🧪 How to Judge What’s Real: A New Method for UKRI — and Beyond
AI-generated submissions can sound convincing — especially when structured around official scoring rubrics. But when we tested GPT-4o using real-world grant prompts, it often preferred its own polished but superficial answers over original ideas grounded in practical experience.
That changed when we flipped the format. Instead of asking GPT-4o to assign points against a fixed rubric, we simply gave it two submissions — one real, one AI — and asked: Which is better? The result: GPT-4o reliably picked the stronger, human-led concept and did so with high confidence.
This taught us something important. In the age of generative AI, traditional assessment methods don’t work — not because they’re too complex, but because they’re too simple in the wrong way. To make fair comparisons, we must lean into what AI actually does well: reasoning by example.
That’s why we propose a new approach: pairwise comparison. Each entry is tested against another — judged purely on ingenuity, originality, and feasibility. This method:
- Helps expose polished but empty AI-generated submissions
- Identifies outstanding ideas regardless of polish or formatting
- Can be scaled affordably across large grant competitions
In practice, true fairness means every proposal meets every other. A 500-entry call, for instance, creates roughly 500 000 head-to-head match-ups. Run naïvely on GPT-4-turbo that would land near the £10 000 mark—but we found three shortcuts that preserve accuracy while collapsing cost: (1) asymmetric judgement (A vs B once, not twice); (2) a Swiss-style “100 random opponents each” tournament that converges on the same ranking with ~90 % fewer calls; and (3) a GPT-3.5 pre-screen that boots clearly weak or AI-spam entries before the expensive model even looks at them.
Combined, the two-phase recipe—50 000 cheap comparisons, followed by 2 450 GPT-4 finals on the top 50—lands at about £158.40 today, and will keep sliding as model prices fall. In other words, for less than one train ticket to Swindon you can triage a £15 million competition, surface the best 10 %, and give human judges a clean, confidence-scored shortlist.
The implications go far beyond grant funding. This process could work equally well in reviewing PhD proposals, published research, or startup accelerator applications. Anywhere a written submission is judged on merit, this method applies.
Crucially, it’s not about filtering AI out — it’s about recognising substance over style. We’re not arguing against AI involvement. In fact, our own entry — co-written by Nick Ray Ball and GPT-4o — was stronger because of that collaboration. But what we’ve proven is this:
Even the best human ideas can be beaten by generic AI if judged the wrong way.
But judged the right way — the meaningful way — the real thing wins.
To read the full testing breakdown, cost analysis, and future roadmap:
🏛️🔬 UKRI AI Grant Assessment System
The Human + AI Network That Fixes Public Sector Communication
Introducing Sienna AI 🏛️📡GOV-COMMS O🌀KRs
Our pair-wise GPT test—part of the 🏛️🔬 UKRI AI Grant Assessment System—proved that pitting every submission head-to-head with the prompt “Which is better?” can shield a £9 billion grant programme from AI-powered chancers for the price of a long lunch.
The next move is to weave in real expertise. GOV-COMMS builds a live “intelligence network” that routes every application to people who actually know the field, captures their verdict, then loops that insight back through the same AI filter before anything reaches an assessor’s desk.
One proposal, four passes — AI → human → AI → (optional) final judge — closing blind spots and slashing the administrative drag that swallows two-thirds of Innovate UK’s budget.
From here, we step inside GOV-COMMS itself.
The problem with AI-generated grant applications and UKRI isn’t just the AI. It’s the fact that no human judge — no matter how smart — can be an expert in every field. UKRI has thousands of submissions crossing dozens of disciplines, yet it relies on assessors who in the vast majority of cases have no expertise in the specialisation that the competition entry resides.
That’s why we propose a parallel human network to work alongside the 🏛️🔬The UKRI AI Grant Assessment System. A second layer of review drawn from real-world experts, volunteers, professionals — people who know what they’re reading and can spot brilliance (or nonsense) before it gets buried in bureaucracy.
The concept draws directly from our 🏛️📡GOV-COMMS OKRs framework — a five-part blueprint to transform how governments communicate, respond, and govern in the 21st century.
GOV-COMMS reimagines how governments receive, categorise, route, and respond to every message from the public. Anyone who has attempted to contact a UK government department knows how dysfunctional the current process is. Layers of screening protect civil servants—not to facilitate resolution, but to deflect engagement and responsibility. Direct communication is virtually impossible. This leaves most citizens with a single, inadequate alternative: appealing to their local political representative, who is unlikely to fully grasp the specific context or technical details of their enquiry.
GOV-COMMS changes all of that. It creates a structured response network — starting with political party volunteers and scaling into government departments — where every incoming communication is tagged, routed, and triaged to the right person based on expertise. Not the nearest inbox. The best-qualified brain.
2096g2c7b) May 5, 2025
ℹ️ Important clarification: GOV-COMMS has been written to assist a political party in winning an election. This is because bureaucracies — and to an extent the civil service — operate at a glacial pace and do not appreciate transparency. Therefore, the implementation method is to encourage political parties to adopt the system. Once in use, whichever party is in power will simply mandate its adoption across government. That is the path to entry.
The presentation begins with 1. 📡 What is GOV-COMMS OKRs?, explaining how this initiative evolved from the AI-generated ALL-COMMs into Sienna AI Gatekeeper — with GPT-4 (Sienna 4o) acting as a hybrid super-intelligent receptionist-editor. From there, we introduce 2. 🧠 The Political Intelligence Network — a national database of every party member’s specialist subjects. When enquiries are received, Gatekeeper routes them intelligently to those best equipped to respond.
Next, 3. 🌀 How GOV-COMMS 'O🌀KRs' Work explores the full operating logic of this political OS — harmonising top-down strategy with bottom-up participation. Here, we outline the campaign’s three overarching objectives:
- ⚔️ Hold all current seats
- 🏹 Identify, attack, and win the swing constituencies needed to govern
- 🎥 Win the media war (social and traditional)
Beneath these sit the Milestone Objectives — the critique and training ground for every government department, offering parties a chance to prepare to govern. Each individual seat becomes a Milestone Key Result, and beneath that, tens of thousands of Key Results define the actions required to secure victory.
4. 🎛️🔁 Live Feedback for Leadership and The O🌀KRs Points Leaderboard for Personnel shows how each Key Result is tracked, recognised, and scored — turning civic participation into a gamified, real-time feedback system. Points, progress, and leaderboards drive morale and highlight every contributor's effort in real time.
Finally, 5. 🏛️📡 GOV-COMMS O🌀KRs for Political Parties — and Governments outlines how this system — once proven inside parties — could become the foundation of future government communication in the UK. And if adoption by GDS proves unlikely, we’re already looking to other nations: 🇿🇦 South Africa, 🇪🇺 Europe, and 🇺🇸 the United States — where the appetite for government efficiency and innovation is rising fast.
1. What is 🏛️📡GOV-COMMs O🌀KRs?
GOV-COMMS OKR Gatekeeper is the AI-powered heart of the GOV-COMMS platform — a system that redefines how political parties receive, process, and respond to communication. Inspired by the Sienna AI Gatekeeper framework and built upon the architecture of S-Web 6 VC AI CMS and ALL-COMMs, it brings together smart summarisation, keyword routing, and OKR alignment into one unified civic infrastructure.
The idea began with the breakdown of communications within the Labour Party — where local representatives like Stuart Gosling in Epsom had no way to share ideas or concerns with party leadership. This internal silence exposed a national flaw: political parties were disconnected from their own base, and by extension, the people.
If the leadership sits in a room without structured input from their members or voters, bureaucracy and inefficiency take control. GOV-COMMS aims to fix that.
At its core is Gatekeeper — a GPT-4 AI (Sienna 4o) acting as a hybrid receptionist-editor. When a constituent sends in a long, complex or disorganised message, Gatekeeper first stores the full version in its internal memory. Then it begins a dialogue — helping the user distill their message into a more usable format:
- 🔑 A six- or seven-word menu keyword
- 🧠 Meta Title
- 📝 Meta Description
- ✍️ 400-word summary prompt
- 📄 1000-word full version prompt
- 📂 Additional info or attachments
This structured input becomes a standardised communication object. It can then be routed to the most relevant person in the party, stored and surfaced on the appropriate S-Web 6 VC website (If desired every single member can have their own website) or even linked into ongoing conversations.
Messages aren’t thrown into the void — they’re summarised, tagged, and connected to live conversations. When a similar topic is raised by someone in leadership, speaking directly to Sienna AI — the system uses Swapping Menus Function logic to detect the menu keyword. The corresponding meta title and description are immediately recalled into the conversation. If this short sentence intrigues the listener, the AI senses relevance and auto-injects the 400-word version, and if still intrigued, the 1000-word version too — then providing full context and history.
In this way, the conversation memory becomes an active, living database. All contributions become part of the collective decision-making process.
And because every structured submission maps directly to OKRs, Gatekeeper becomes the first point of real action: connecting citizens to party strategy through objectives, milestones, and micro-contributions.
GOV-COMMS OKR Gatekeeper doesn’t just organise ideas — it turns communication into policy, and citizens into contributors.
ℹ️ The following was originally tailored for a political party and its members. Expanding this framework to government would broaden the audience to include everyone in the UK, while the political intelligence network would be open to any interested participant—initially drawing from party members, but not limited to them.
2. The Political Intelligence Network 🧠🌐
At the heart of GOV-COMMS is a radical but simple shift: treat every party member as a potential source of expertise. This section outlines how GOV-COMMS builds a searchable database of each member’s specialist knowledge and routes incoming communication to the most relevant responder — not just the local MP.
This problem isn’t theoretical — it was lived. In 2012, Sienna AI founder Nick Ray Ball brought a series of advanced economic theories — including “Sparta Rises Again” and “Cities of Science” — to his local MP, then-Justice Minister Chris Grayling. These theories would later form the basis for AmericanButterfly.org. But instead of being welcomed, they were dismissed. Years later, it became clear why: Grayling had no economics background, and was strongly Eurosceptic — a poor match for the proposal. The system had failed because the right person never saw the message.
GOV-COMMS fixes this by tagging each incoming message with keywords and routing it to the person in the party best equipped to understand and respond. That could be an MP, a candidate, or a passionate member from a different constituency entirely.
To make this possible, GOV-COMMS invites every member to register their areas of knowledge and interest. These tags power a national routing system, forming a modern successor to the Open Directory Project (ODP) the engine behind Google's early success — which Nick Ray Ball contributed to in 2001 before Google’s PageRank took over. Each tag is accessible via connected S-Web websites, forming a decentralised political knowledge graph.
When someone calls — as demonstrated in the ⚕️⚛️GP-AI Gatekeeper example — Gatekeeper will politely guide the conversation into an actionable memo, often solving the issue on the spot. Whether by phone or in writing, Gatekeeper first determines whether the issue is local or national. A complaint about a neighbour’s hedge? Logged in the database and routed to the local MP’s assistant. A concern about AI ethics, water pollution, or any topic on a national scale — from Economic AI to the migratory patterns of red kites — is routed via keyword matching to a relevant party member. If no one is specifically tagged, it escalates to high-engagement or generalist contributors.
Crucially, the conversation doesn’t end with a reply, it becomes part of the GOV-COMMS knowledge base — a public or internal-facing page on an S-Web site tied to the relevant menu keyword. That way, when someone else raises the issue, they’re shown the conversation and invited to build upon it.
Each page grows into a live thread of ideas, contributions, and rebuttals — all credited to those involved. Over time, these pages become both reference and roadmap, and each entry can be linked directly into GOV-COMMS OKRs: objectives, milestones, and key results.
This is how GOV-COMMS decentralises expertise and transforms a political party into an active, intelligent network — where good ideas never die, and the best response always wins.
ℹ️ This is the end of the extract from: 🏛️📡GOV-COMMs O🌀KRs
To read the three concluding sections, please follow the link:
Bureaucratic Inefficiency Equaliser
by adapting the gov coms design from assisting political parties to assisting all government departments and bureaucracies we provide another solution for the government Digital Service....
we've had a good look at five government bureaucracies, we've already presented CMS solutions that would benefit HM courts and tribunals and HMRC. however the NHS the Department of Work and Pensions and the Research and Innovation departments are almost kleptocracies, utterly uncontactable by the public, be that to complain or be that to provide a solution to a problem. in all cases unlike software engineering where feedback on errors is utterly essential All of the above seem to actively bury complaints indeed the NHS have a department NHS are who are supposed to learn from mistakes but actually seem Promote defensive medicine [that's medical record fraud ] across the entire NHS, for more about a fleet of lawsuits we are introducing to raise awareness of this problem and essentially read the UK public of the NHSR see: https://siennaai.net/T2/TLS-W/
the Department of Work and Pensions is an uncontactable animal relying on 20th century technology and deliberately suppressing complaints in exactly the same way as the NHS, only lawsuits on big scales will ever get through to that bureaucracy. as for ukri after completing the Innovate UK process we found a number of suspicious circumstances that led us to write
2090j2) ⚛️🕵️ 11 Reasons Why I Suspect Fraud at Innovate UK and How to Fix It [28 Feb 2025]
https://siennaai.net/docs/t10t/2090j2
whilst in the process of writing the above Innovate UK reached out to me asking if I would participate in a survey about their communications. I agreed, and brought a number of the points raised above alongside a number of very specific communication failures to Communications Manager Mark Wilson. mark's reaction was not unexpected but disappointing, it seems his entire department is a whitewash, not one single point that I raised was agreed to be valid, this will in future be discussed in various lawsuits, as it seems the only way to fight this bureaucracy is not via complaints departments that are self serving and are only there to give the appearance of proprietary But it is to launch a lawsuit for every infraction.
the most disturbing of the eleven reasons why I suspect fraud at Innovate UK is they're financing
One data point: UKRI has received approximately £60 billion since 2016, yet only £20.4 billion has reached innovators. The remaining two-thirds appears to vanish into “administration.” This is not speculation — it’s grounded in data. The UKRI grant recipient spreadsheet, which lists all 45,000 grant winners since 2016, shows an average award of £453,333 per grant. Notably, this dataset was buried deep within the UKRI website, requiring significant investigative effort to locate. When asked to clarify the discrepancy between total funding and disbursement, UKRI did not respond.
While the 66% waste figure does not exceed the aid leakage cited in The Bottom Billion — where countries like Chad saw only 5–10% of aid reach recipients — it does rival the dysfunction that preceded the fall of the Afghan government in 2021, a regime widely regarded as a kleptocracy. If UKRI’s performance is worse than that of the Afghan government before its collapse, then by functional standards, it may warrant the same distinction.
whilst we do not have the figures to present equal arguments on other government bureaucracies we can say for certain that they all have the same problem , departments within that are there to give the appearance of due diligence and efficiency that are hiding any negative results to the severe detriment of the UK taxpayer and the British Economy.
whilst there are many ways to improve efficiency across the board using the six module design and the 10 technologies the simplest of all is simply making a communication system that works and that is Gov Comms, potentially the most important of all of the GDS gov.Uk CMS solutions, where we include communications as Content within the management system of the CMS Although the nudge CRM-AI, customer relationship management systems are of course equally bundled into this solution.
99.9% of all government communications are ignored, misunderstood, or buried — not because they lack value, but because they reach the wrong person. The system is not designed to help. It is designed to deflect.
To fix UK innovation, we must fix UK communication. That’s the promise of GOV-COMMS. And that’s the starting point for real democratic AI — where the people are finally heard, understood, and responded to with intelligence.
🏛️📡GOV-COMMs O🌀KRs + 🏛️🔬UKRI AI Grant Assessment System
as for improving the UKRI grant system itself bundling the UKRI Grant Assessment System and gov coms together is the only way UKI will be able to assess grants prepared by AI scammers
Instead of AI acting alone — or human judges floundering in unfamiliar fields — we propose something stronger: a collaborative intelligence layer. The AI pre-scores. The network pre-vets. And only then does the submission go to final judges, with context, confidence scores, and real signal instead of AI-generated noise.
⚕️⚛️🕵️The £112-£147 billion due diligence challenge - For Dr Dawn Geatches to Part 2
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?
