Donald Leka

Founder @JumptuitNow Genesis J2T, AI-Powered Global Event Detection, Tracking the continuously changing constitution of elements, motion & trajectory of events

[Donald Leka @DonaldLeka]

Beyond LLMs: How Jumptuit's Don Leka Is Building AI for the Physical World https://t.co/4KRq8vcgsD

In this episode of Intel at the Edge, host Chip Usher sits down with Don Leka, Founder and CEO of Jumptuit, to explore a different kind of artificial intelligence — one built not on text, but on real-time data from the physical world. Don shares his journey from building the Glide OS cloud platform in 2004, to founding Jumptuit, a company using a large dynamic reasoning model to track physical-world signals — from shipping movements and port activity to refinery output and geopolitical indicators — in near real time. The conversation covers:

Why Jumptuit's approach differs fundamentally from large language models

How cross-sector data intersections can reveal probable events before they happen
Using comparative, causal, and analogical reasoning to forecast geopolitical and economic shifts
Case studies: Russia's GDP resilience despite sanctions, shifting oil demand, and instability in Niger and Albania
What the intelligence community could gain from live, empirical, physical-world data
A must-listen for anyone interested in AI, geopolitical forecasting, and the future of intelligence analysis beyond large language models.

[Donald Leka @DonaldLeka]

Jumptuit's AI Patent Promises Health Alerts Innovation - Investors Hangout

“Jumptuit’s patent might be the start of something that adds value by literally keeping us informed of the unseen risks around us.”

“The fact Jumptuit is targeting a combo of health, life, and environmental sciences signals a much broader horizon. There's the allure of crafting systems that actually prevent medical issues before they play out by giving folks a heads-up they wouldn’t otherwise get. “

“a genuine application that mitigates health risks before they mature can shift some serious weight in both healthcare and environmental circles.”

https://t.co/SRf1HHv2za

[Donald Leka @DonaldLeka]

Jumptuit Secures USPTO Artificial Intelligence (AI) Patent for Health, Life, and Environmental Sciences and Exposomic Research

We are pleased to announce that the U.S. Patent and Trademark Office (USPTO) has awarded Inventor Donald Leka, Founder and CEO of Jumptuit, the following AI patent, “AI Systems and Methods for Environmental Exposure and Early Health Risk Alerts.”

This invention relates to a system and method of AI individualized forecasting for environmental exposure and early health risk alerts. AI analyzed dynamic environmental, disease, and toxin exposure variables derived from recent geolocation data yield early individualized notifications including coupled-variable risk assessments. AI generated forecasts expand health risk evaluation to include future localized conditions not yet occurring at a user's location, but rather based on pattern recognition of variable trends, sequence, and velocity relative to an individual's unique health profile. Each set of AI forecasted exposure variables constitutes a dynamic, geolocation specific predictive model rather than a static threshold alert based on generalized historical environmental data.

For genomic research, mapping environmental exposures in real time bridges the gap between static genetic predisposition and gene expression, revealing how dynamic physical variables interact with epigenetics to trigger disease manifestation.

The newly granted patent, “AI Systems and Methods for Environmental Exposure and Early Health Risk Alerts” (Patent No. 12793535), expands on the previously granted patents, System and Method of AI Assisted Search Based on Events and Location” (Patent No. 12561386), “System and Method of AI Assisted Search” (Patent No. 12361080), “System and Method of Correlating Multiple Data Points to Create a New Single Data Point” (Patent No. 11977584), “System and Method of AI Assisted Search” (Patent No. 11971933), and “System and Method of AI Assisted Search Based on Events and Location” (Patent No. 11803602).

“The potential to transition from historical exposomic analysis to continuous predictive observation represents a fundamental shift for health, life, and environmental science,” said Inventor and Jumptuit Founder and CEO, Donald Leka. “By dynamically mapping the velocity and interaction of environmental, disease, and toxin variables, Jumptuit empowers researchers, healthcare providers, and public health officials to move from reactive mitigation to proactive, life saving intervention before environmental threats fully materialize. This invention also enables individuals to anticipate compound environmental risks and how they impact their unique medical, biological, and lifestyle health profiles, providing them with the ability to take agency in relation to their health.”

[Fermat's Library @fermatslibrary]

As of today Voyager 1 is one light-day from Earth - 25.9 billion km (16.1 billion miles) away.

A radio signal now takes 24 hours to reach it. In ~50 years, it has traveled about 1/1500th of the way to Alpha Centauri, our nearest star at 4.27 light-years.

[NASA @NASA]

We mourn the passing of Patrick Stewart, who inspired countless individuals at NASA (and beyond) to boldly go where no one has gone before.

[Donald Leka @DonaldLeka]

AI's Missing Link to the Physical World: Donald Leka at Columbia University Disaster Readiness Summit 2026

Watch: Jumptuit Group Founder and CEO Donald Leka at the AI 4 Resiliency Panel at the Disaster Readiness Summit, hosted by Columbia University’s National Center for Disaster Preparedness (NCDP) at Teachers College and led by the Global Enabling Sustainability Initiative (GeSI).

Transcript: “So, I think we have to be cautious when we use the term AI. It's an umbrella term...and when most people refer to AI, they're really talking about Large Language Models.

And Large Language Models are really historically focused, and so there are certain limitations. They do not really bring you to the present moment in time, and they don't engage in the type of quantitative reasoning that would enable us to really be able to predict events...

Of course, there's a lot of hype now with the IPOs that are coming out as well, so it's important to keep that in mind.

What I would say is look back at the genome research that was done, look at what people were saying about that 30 years ago. And what they were saying was that by somewhere between…the year 2020 and 2030 – most major diseases would be eliminated. Has that happened? No.

Okay, and one of the reasons it didn't succeed, and the critical piece to understand, is that it did not take into account the exposome, which is all the physical factors that affect health and affect people. And so that now is calculated to be over 80% of what determines whether or not you get diseases...

Of course…there is the climate factor. As temperatures go up, that actually facilitates diseases…to fester and grow.

So what was missing was the exposome, which is all of those external types of variables and factors.

And when you look at LLMs and you look at the benchmarks for them, there is not a single benchmark – and you can go do the research…we had teams at Yale and Georgetown do extensive research – and they could not find a single benchmark test that was measuring success in the physical world. So it's all theoretical, it's test-taking, it's automation of digital tasks, but all of it functions in the digital world.

100% of human beings live in the physical world. No human beings live in the digital world.

So that's a critical thing to understand, and the vast majority of the global economy – about 85% of it – resides in the physical world. So it's critical that this transition to understanding the physical world, and understanding those external variables, and the diversification of AI models occurs in order for us to achieve our objectives." - Donald Leka

[Donald Leka @DonaldLeka]

Jumptuit Expands AI Intellectual Property Portfolio with USPTO Notice of Allowance for Multidimensional Data Correlation and Native Security and Verification Architecture

Jumptuit, an Anticipatory Intelligence research and development company, today announced that the U.S. Patent and Trademark Office (USPTO) has issued a Notice of Allowance broadening protection of Jumptuit's novel Physical World Artificial Intelligence.

The invention disclosed in Application No. 18/426,166, titled “System and Method of Correlating Multiple Data Points to Create a New Single Data Point,” details an active, multidimensional data correlation engine. The inventor is Jumptuit Founder and CEO, Donald Leka.

The invention provides a framework for continuous analysis of relationships across a plurality of data points dispersed across multiple metadata dimensions, and for autonomously generating non-linear data correlations into new single correlated data points. The invention discloses security and verification protocols (e.g., encryption, access control, and transaction verification) embedded directly in the data correlation, search engine indexing, and metadata processing pipelines (rather than as external, post hoc security layers), securing multi-source metadata without compromising primary data locations.

“We are pleased to receive the Notice of Allowance from the USPTO broadening protection of Jumptuit’s Physical World Artificial Intelligence systems,” said Inventor and Jumptuit Founder and CEO, Donald Leka. “This novel technology at the molecular data level is a meaningful addition to Jumptuit’s IP portfolio that constitutes the underlying systems and methods of the Company’s technology with significant implications for transforming how non-linear data correlations are discovered and how data across disciplines, sectors and dimensions is used and protected at all stages.”

Learn More: https://t.co/Vhs3638jC1

[Donald Leka @DonaldLeka]

I look forward to participating in the AI 4 Resiliency Panel at the Disaster Readiness Summit, hosted by Columbia University’s National Center for Disaster Preparedness (NCDP) at Teachers College and led by the Global Enabling Sustainability Initiative (GeSI).

Day 1 of the Disaster Readiness Summit (Tuesday, September 22) will explore how communities, leaders, and practitioners can strengthen climate resilience through anticipatory action, artificial intelligence, and disaster readiness.

[Jumptuit @JumptuitNow]

30% of Global Container Shipping Under Threat as Energy and Chemical Prices Surge

Simultaneous Disruptions at Hormuz and Bab el-Mandeb Drive Up Global Inflationary Pressures and Supply Chain Risks

De-Risking Mission-Critical Decisions and Preventing Cascading Physical World Failures

Physical World AI: Bridging the Disconnect | AI+ Expo 2026 | Jumptuit Group Founder Donald Leka

How can AI accurately predict physical-world outcomes? Anticipatory Intelligence bridges the gap between digital data and real-world execution.

The Physical Disconnect: Why current Large Language Models (LLMs) struggle with real-world physical dynamics.

Large Dynamic Reasoning Model (LDRM): How dynamic non-verbal reasoning, including sign, comparative, causal, analogical, and temporal reasoning systems and methods, improves physical world forecasting.

Human Decision-Making: Using LDRM Physical World AI to improve real-time strategy and forecasting to anticipate cross-sector risk.

Watch: https://t.co/opXc2vqX7a

[Donald Leka @DonaldLeka]

30% of Global Container Shipping Under Threat as Energy and Chemical Prices Surge

Simultaneous Disruptions at Hormuz and Bab el-Mandeb Drive Up Global Inflationary Pressures and Supply Chain Risks

De-Risking Mission-Critical Decisions and Preventing Cascading Physical World Failures

Physical World AI: Bridging the Disconnect | AI+ Expo 2026 | Jumptuit Group Founder Donald Leka
How can AI accurately predict physical-world outcomes?
Anticipatory Intelligence bridges the gap between digital data and real-world execution.

The Physical Disconnect: Why current Large Language Models (LLMs) struggle with real-world physical dynamics.

Large Dynamic Reasoning Model (LDRM): How dynamic non-verbal reasoning, including sign, comparative, causal, analogical, and temporal reasoning systems and methods, improves physical world forecasting.

Human Decision-Making: Using LDRM Physical World AI to improve real-time strategy and forecasting to anticipate cross-sector risk.

Watch: https://t.co/91dERESPTA

[Special Competitive Studies Project @scsp_ai]

🎙️In this episode of #IntelattheEdge…

Host @ChipUsher talks with @DonaldLeka, Founder & CEO of @JumptuitNow, about a different kind of AI — one built not on text, but on real-time signals from the physical world.

From launching Glide OS in 2004 to building Jumptuit's large dynamic reasoning model, Don explains how tracking shipping, port activity, refinery output, and geopolitical indicators can reveal probable events before they happen.

A must-listen for anyone into AI, geopolitical forecasting, and the future of intelligence analysis beyond large language models.

Listen now:
📺 https://t.co/5eWjbop0cL
🍎 https://t.co/puChbEzBZw
🎧 https://t.co/1JaMMRzToG

[Donald Leka @DonaldLeka]

Beyond LLMs: How Jumptuit's Don Leka Is Building AI for the Physical World https://t.co/4KRq8vcgsD

In this episode of Intel at the Edge, host Chip Usher sits down with Don Leka, Founder and CEO of Jumptuit, to explore a different kind of artificial intelligence — one built not on text, but on real-time data from the physical world. Don shares his journey from building the Glide OS cloud platform in 2004, to founding Jumptuit, a company using a large dynamic reasoning model to track physical-world signals — from shipping movements and port activity to refinery output and geopolitical indicators — in near real time. The conversation covers:

Why Jumptuit's approach differs fundamentally from large language models

How cross-sector data intersections can reveal probable events before they happen
Using comparative, causal, and analogical reasoning to forecast geopolitical and economic shifts
Case studies: Russia's GDP resilience despite sanctions, shifting oil demand, and instability in Niger and Albania
What the intelligence community could gain from live, empirical, physical-world data
A must-listen for anyone interested in AI, geopolitical forecasting, and the future of intelligence analysis beyond large language models.

[Jumptuit @JumptuitNow]

Beyond LLMs: How Jumptuit's Don Leka Is Building AI for the Physical World https://t.co/igaDdwTH1w

In this episode of Intel at the Edge, host Chip Usher sits down with Don Leka, Founder and CEO of Jumptuit, to explore a different kind of artificial intelligence — one built not on text, but on real-time data from the physical world. Don shares his journey from building the Glide OS cloud platform in 2004, to founding Jumptuit, a company using a large dynamic reasoning model to track physical-world signals — from shipping movements and port activity to refinery output and geopolitical indicators — in near real time. The conversation covers:

Why Jumptuit's approach differs fundamentally from large language models

How cross-sector data intersections can reveal probable events before they happen

Using comparative, causal, and analogical reasoning to forecast geopolitical and economic shifts

Case studies: Russia's GDP resilience despite sanctions, shifting oil demand, and instability in Niger and Albania

What the intelligence community could gain from live, empirical, physical-world data

A must-listen for anyone interested in AI, geopolitical forecasting, and the future of intelligence analysis beyond large language models.

[Donald Leka @DonaldLeka]

Beyond LLMs: How Jumptuit's Don Leka Is Building AI for the Physical World https://t.co/KrJxVgusAK

Beyond LLMs: How Jumptuit's Don Leka Is Building AI for the Physical World

In this episode of Intel at the Edge, host Chip Usher sits down with Don Leka, Founder and CEO of Jumptuit, to explore a different kind of artificial intelligence — one built not on text, but on real-time data from the physical world.

Don shares his journey from building the Glide OS cloud platform in 2004, to founding Jumptuit, a company using a large dynamic reasoning model to track physical-world signals — from shipping movements and port activity to refinery output and geopolitical indicators — in near real time.

The conversation covers:
Why Jumptuit's approach differs fundamentally from large language models
How cross-sector data intersections can reveal probable events before they happen
Using comparative, causal, and analogical reasoning to forecast geopolitical and economic shifts
Case studies: Russia's GDP resilience despite sanctions, shifting oil demand, and instability in Niger and Albania
What the intelligence community could gain from live, empirical, physical-world data
A must-listen for anyone interested in AI, geopolitical forecasting, and the future of intelligence analysis beyond large language models.

[Donald Leka @DonaldLeka]

Jumptuit Expands AI Intellectual Property Portfolio with USPTO Notice of Allowance for “AI Systems and Methods for Environmental Exposure and Early Health Risk Alerts”

Jumptuit, an Anticipatory Intelligence research and development company, today announced that the U.S. Patent and Trademark Office (USPTO) has issued a Notice of Allowance broadening protection of Jumptuit's novel physical-world Artificial Intelligence.

The invention, titled “AI Systems and Methods for Environmental Exposure and Early Health Risk Alerts,” provides dynamic AI-generated, individualized health forecasts and alerts. The inventor is Jumptuit Founder and CEO, Donald Leka.

The system continuously synthesizes disparate data streams: (1) geolocation data, including future times and locations derived from current movement, trajectory, and scheduled travel; (2) exposure variable data, including environmental conditions, diseases, and toxins; and (3) individual health characteristics, including pre-existing conditions, medications, and dynamic health data, to generate uninterrupted health risk assessments. The system observes variable trends in real time and evaluates combined "coupled variables" against personal health thresholds to predict risks before the individual is exposed.

"We are pleased to receive this Notice of Allowance from the USPTO broadening protection of Jumptuit's physical-world Artificial Intelligence systems,” said Inventor and Jumptuit Founder and CEO, Donald Leka. "This novel technology is a meaningful addition to Jumptuit's AI IP portfolio that constitutes the underlying systems and methods of the Company's technology with significant implications for transforming how data is discovered and used, and broad applications across sectors and disciplines – and to empower people with individualized intelligence in their daily lives.”

Learn More: https://t.co/jbERmnXC2V

[Donald Leka @DonaldLeka]

Predicting the Shock: Leveraging Artificial Intelligence for European Energy Security

By Dylan Alexander Leka
Senior Director of Geopolitical and Geospatial Data Analysis

For decades, European energy policy operated on the fragile assumption of global market stability, leaving the continent acutely vulnerable to sudden geopolitical crises. Rather than building resilient, long-term strategic energy infrastructure capable of weathering black swan events, policymakers prioritized short-term market-driven efficiencies, goal-based decarbonization green-energy initiatives, and slow-paced energy transitions.

Europe’s structural vulnerability was compounded by domestic political realignments on energy, most notably Germany’s Energiewende policy, which culminated in the final shutdown of its remaining nuclear power plants in April 2023. By taking its largest source of reliable, baseload, zero-carbon energy offline before sufficient renewable storage or grid infrastructure was ready, Germany inadvertently deepened its reliance on fossil fuels and foreign energy imports, especially from the Russian Federation and Gulf Cooperation Council (GCC) states.

Read More: https://t.co/F5ogxRaASY

[UAE Mission to the UN @UAEMissionToUN]

Trusted evidence must move as quickly as the world itself.

At the side event "Fast Forwarding SDGs through Data-Driven Global Collaborations," the UAE 🇦🇪 reaffirmed that official statistics remain the foundation of evidence-based policymaking, while highlighting the role of artificial intelligence and next-generation data in delivering more timely, connected, and responsive evidence for decision-making.

The UAE also underscored that building the next generation of data ecosystems requires stronger partnerships among governments, international organizations, academia, the private sector, and innovators to support more resilient and effective sustainable development beyond 2030.

[Donald Leka @DonaldLeka]

I am looking forward to the Future Investment Initiative’s (FII) FII Priority Europe summit, 'Europe Reimagined: Capital, Sovereignty & Strategic Autonomy,' on June 17–19, 2026 in Rome, Italy.

Europe has never faced a more critical need for reliable anticipatory intelligence.

By anchoring AI — driven by unbiased cross-sector data to understand context, and advanced reasoning to forecast consequence — at the core of strategic planning and policy formulation, we can transform insight into active, anticipatory preparedness.

Jumptuit Founder and CEO Donald Leka at FII on the Ethos of Observational AI:

Watch Video: https://t.co/pQV47J9mnS

Is there an ethos you live your life by?

Donald Leka: “The ethos that I live my life by, and the technology very much reflects those values, is neutrality of data. It’s very important that the data not become biased or manipulated in any way because otherwise we’re going to be making some very dangerous decisions that could have a very negative impact. So, the sanctity of data is the core value of the company and it’s based on my values as well.

The ethical component – ethical AI – is very important, but it has to be applied after we have a clear picture of what realistically can be done.”

[Donald Leka @DonaldLeka]

How to Fix AI’s Reasoning Problem

The Problem: The physical world is driven by deeply interconnected, complex systems that trigger cross-sector ripple effects across the global economy, infrastructure, and supply chains. However, traditional Large Language Models (LLMs) fail to map and forecast these intricate cross-sector collateral consequences.

The Solution: Jumptuit has developed a flexible reasoning process that dynamically shifts among sign, comparative, causal, and analogical reasoning. This allows the AI model to observe, analyze, and understand live, cross-sector data streams from the physical world with sophisticated cross-sector ontologies enabling it to detect novel, complex conditions and anticipate events.

By dynamically adapting to live inputs, these autonomous reasoning permutations mitigate systemic risk and reduce the costs of geopolitical and environmental instability, augmenting human decision-making and forecasting event shockwaves before they materialize.

Jumptuit Founder & CEO Donald Leka at SCSP AI+ Expo 2026:

“Jumptuit observes phenomena in the physical world. We track events, transitional events, and events in motion, as well as probable events. Because clusters of signals appear before an event occurs, Jumptuit captures these precursors, antecedent elements, and human activity—including human behavior and human systems.

We treat human behavior and human systems as external variables to be observed.

Jumptuit utilizes various reasoning types. Depending on the live data input, we apply different permutations of sign, comparative, causal, analogical, verbal, and temporal reasoning.

By doing so, Jumptuit observes, analyzes, and understands live cross-sector data, ensuring our reasoning permutations are dynamically responsive to live inputs.

Verbal reasoning is ultimately the final stage. Once a core data response is formulated based on the live data input, verbal text is generated to describe the findings.”

Jumptuit’s Large Dynamic Reasoning Model (LDRM):

Dynamic Observation: Phenomena, Events, Transitional Events, Probable Events, Human Systems, Human Behavior

Dynamic Reasoning Methods: Sign, Comparative, Causal, Analogical, Verbal, Temporal

Autonomous Reasoning Permutations Dynamically Respond to Live Inputs

Detect Novel and Complex Transitional Events

Generate Continuous Dynamic Scenario Forecasting of Probable Events

Next Up: Why LLMs Failed to Accurately Forecast Iran’s Strategic Response to Operation Epic Fury and the Conflict’s Duration

[Donald Leka @DonaldLeka]

How to Fix AI’s Data Problem

Jumptuit Founder & CEO Donald Leka at SCSP AI+ Expo:

Data Pipeline Flow and Cross-Sector Analysis:

“Jumptuit starts with Live Homogeneous Vector Data Pipelines. These pipelines are organized by Aerospace and Defense, Food and Agriculture, Energy, Environmental for Physical Phenomena, Finance, Manufacturing, Metals and Mining, Public Health, Transport and Logistics, and many other vertical sectors.

All of that data flows into a Unified High-Dimensional Vector Space. This is where the cross-sector analysis occurs, and cross-sector risk indices are generated.

This is also where intelligent associations are created within the data itself – mathematical indicators, numerical vectors with magnitude and direction, benchmarks and thresholds, data correlations, and coupled variables. Meaningful, intelligent insights are formed here, stored in the system, and can be accessed when faced with future similar probable events.”

Data Constitution:

“Jumptuit focuses on observable phenomena and human activity, primarily quantitative data. Our data mix is 80% quantitative and 20% qualitative. We have connected to hyper-localized quantitative data sources all over the world.

In contrast, LLMs rely primarily on web crawlers to capture their content.

So the accuracy level is higher because we are looking at empirical data. The objectivity is greater because we are looking at things that are measurable and verifiable, as opposed to human and AI-generated content, which is to a large extent unverifiable. And the consistency in terms of the methodologies we apply – because it is quantitative data and from verifiable sources which are primarily observation-based – makes a huge difference.”

Triangulation:

“Jumptuit uses triangulation from multiple sensors and audited, verifiable data sources. When we constitute a picture of what is happening in relation to an event in the world, what we are looking at is multiple data sources to construct that picture.”

This ensures the picture is built from various independent perspectives rather than a single source. If a satellite sensor, a ground-level IoT sensor, and a financial ledger all point to the same event, the confidence interval increases significantly.

Next Up: How to Fix AI’s Reasoning Problem