The Taiwan Strait and East China Sea are regions of high geopolitical tension. In these areas, the United States faces a significant challenge: monitoring China’s mobile Anti-Access/Area Denial (A2/AD) systems. Such systems are designed to prevent or deter an adversary from entering or operating in a specific area. These systems include missiles, sensors, and anti-satellite technologies. They are designed to restrict U.S. military access to strategically critical areas, threatening American interests and regional stability in the Indo-Pacific (Air & Space Forces Magazine, 2022). The U.S. relies on space-based Intelligence, Surveillance, and Reconnaissance (ISR) systems to track these threats. However, a key limitation, the periodicity predicament, reduces their effectiveness. Gaps between satellite passes, ranging from hours to days, allow China’s mobile assets, such as Transporter-Erector-Launchers (TELs), to relocate undetected (Air University’s Wild Blue Yonder, 2023). These persistent gaps could undermine U.S. strategic interests in this vital region, which is home to Taiwan and major trade routes. This article explores innovative strategies to enhance surveillance and ensure continuous monitoring.

High above Earth, A constellation of satellites continuously surveils the planet, providing critical intelligence to the United States. These systems mark a significant shift from airborne ISR, introducing new capabilities and challenges in global security. The U.S. Department of Defense is leading this transition, moving from airborne Intelligence, Surveillance, and Reconnaissance (ISR) platforms like aircraft and drones to advanced space-based platforms such as the Space Based Infrared System (SBIRS) and commercial satellite constellations. This shift offers unmatched surveillance and strategic benefits but also introduces complexities, costs, and risks that could alter U.S.-China power dynamics.

This article explores the multifaceted nature of this transformation, examining the technological innovations, strategic imperatives, economic burdens, legal dilemmas, operational hurdles, and geopolitical risks that define the move to space-based ISR. At the heart of this debate lies a critical question: how will this orbital ascendance impact U.S.-China relations? Two perspectives frame this discussion. Proponents, including the Department of Defense and its allies, argue that space-based ISR is essential for maintaining U.S. military superiority, particularly in light of China's growing space capabilities. Critics, ranging from arms control advocates to international relations scholars, caution against the vulnerabilities, high costs, and potential for escalating tensions that this shift entails.

Through a narrative that intertwines technological progress, strategic necessity, economic realities, legal and ethical considerations, operational challenges, and geopolitical dynamics, this article aims to illuminate the complexities of this revolution. It is a story of ambition and caution, of opportunity and peril, set against the backdrop of a rivalry that could shape the future of international security.

In a dimly lit Silicon Valley garage, a young coder leans into her laptop screen, her fingers weave lines of code that could one day steer drones through turbulent skies or unlock the tangled code of human DNA. Across the Pacific, in a Shenzhen classroom humming with morning energy, a teenager sketches a neural network on her tablet, her eyes tracing a future where her ideas might reshape cities or cure diseases. These solitary moments, divided by oceans and cultures, share a vital spark: artificial intelligence. As AI redraws the limits of human possibility, the United States and China stand locked in a race not just for technological mastery, but for the minds that will guide its path. Education, once a reliable bridge to stable careers, now faces a pivotal shift, challenged to prepare students for a world where machines think beside us. This journey requires balancing competition with collaboration, all to equip the next generation for an AI-driven age.

Picture a world where the United States no longer stands at the helm of technological innovation, where artificial intelligence, forged in the crucible of China’s ambition, redraws the map of global power. This vision is not a distant echo from science fiction; it looms on the horizon, growing sharper with every step China takes in the AI race. The stakes reach far beyond the realm of code and circuits, striking at the heart of America’s economic lifeblood and the shield of its national security. In a recent conversation, commentator Saagar Enjeti cut through the noise with a stark truth: “America has decided that our retirement plan is number go up,” a shorthand for how our financial future hinges on the soaring tech stocks that drive our markets (Kantrowitz, 2025). To secure that future, the United States must meet China’s relentless rise with unwavering resolve, a strategy rooted in urgency and a clear grasp of what we stand to lose.

A conversation featuring investor Kyle Bass, posted in February 2025 on The Fort – An Entrepreneurship Podcast, offered a stark reminder of the shifting power balance between the United States and China (The Fort – An Entrepreneurship Podcast, 2025). Bass described how Beijing’s rapid rise in advanced manufacturing, semiconductors, and aerospace technology has heightened concerns about intellectual property theft and coerced technology transfers. He warned that America’s continued reliance on critical imports from a chief competitor jeopardizes national security and strains the country’s industrial base. Recent calls for a more confrontational approach toward China underscore the urgency of addressing these risks, particularly in areas where technological innovations can yield both economic progress and military advantages.

Many analysts agree that the United States cannot afford to cede leadership in semiconductors, AI, and other dual-use technologies without putting its broader security at risk (Biden, 2022; Department of Commerce Bureau of Industry and Security, 2022). Proponents of a tougher stance argue that robust export controls, targeted sanctions, and the reshoring of critical manufacturing are necessary measures to shield the country from vulnerabilities. The ensuing debate centers on whether these measures should focus narrowly on high-risk areas or extend more broadly, and on how the United States might reorganize its supply chains without jeopardizing economic prosperity. This article explores China’s emergence as the principal strategic challenger to American technological leadership and explains why more decisive action, particularly in states like Texas, can help preserve U.S. national security, sustain economic innovation, and ensure a healthy industrial base for years to come.

This report investigates emerging human-enhancement technologies for military use, focusing on exoskeletons and neural interfaces. Recent public conversations, such as the forward-looking YouTube discussion featuring Shawn Ryan (2025), underscore growing enthusiasm about the performance gains these innovations might offer to soldiers, along with parallel anxieties about ethical, legal, and geopolitical consequences (Shawn Ryan Clips, 2025). Drawing on prior research that examines prototype hardware, power limitations, global policy debates, and the shifting nature of warfare, this report offers a reasoned appraisal of how these technologies may become integral to defense strategy. It aims to clarify their feasibility, ethical ramifications, and possible influence on military readiness, while suggesting prudent measures for navigating adoption in complex operational environments.

Introduction and Purpose

The past few years have seen the United States impose export controls designed to prevent China from acquiring advanced AI hardware. These controls restrict or downgrade the capabilities of high-performance processors and specialized chips that are typically used to train and run state-of-the-art artificial intelligence models. Observers initially predicted that these measures would significantly hinder Chinese progress in large-scale AI development (CFR, 2025). The emergence of DeepSeek, an open-source large language model (LLM) reportedly trained on hardware that is less powerful than the latest Western GPUs, has complicated this narrative. DeepSeek’s performance claims include success on coding, math, and language tasks that, according to its developers, match those of proprietary Western models such as GPT-4 (CSIS, 2025). These assertions have not yet been independently verified by recognized benchmarks like MLPerf, which is a global initiative that measures AI system performance in areas such as image recognition, language understanding, and recommendation tasks. Nonetheless, DeepSeek’s story has created renewed discussions about whether sanctions might function as a short-term deterrent but a long-term catalyst for indigenous innovation.

This report examines whether these restrictions have inadvertently motivated Chinese AI researchers to concentrate on more efficient, smaller-scale computing infrastructures. It also explores the tension between open-source and proprietary AI releases, questions the broader geopolitical implications of continued technology controls, and considers how early disruptions in professional job sectors suggest that advanced LLMs may alter traditional expectations of automation. While this document references the original research and uses some of its findings, it focuses on providing a narrative that weaves policy considerations, anecdotes from AI development labs, and forward-looking insights into a cohesive picture of the global AI competition.

In recent days, a widely circulated video clip from the Shawn Ryan Show drew public attention to provocative claims by entrepreneur Palmer Luckey, who suggested that if China were to launch an invasion of Taiwan, the United States might deplete its key missiles in just eight days. The same discussion also referenced China’s ability to build ships at an industrial pace purportedly outstripping that of the United States by a ratio of up to 350-to-1. Although neither figure is officially confirmed and both may stem in part from alarmist readings, the resonance of these numbers illustrates genuine concerns: Could the U.S. truly exhaust its missile stockpiles within a week of high-intensity conflict? Does China’s shipbuilding capacity grant it a decisive advantage in maritime power projection? And can next-generation military technologies, such as AI-guided weapon systems, meaningfully offset production shortfalls?

These questions have grown more urgent in light of ongoing analyses by major think tanks, including the Center for Strategic and International Studies (CSIS) and the Heritage Foundation, both of which have published war-game scenarios indicating that U.S. supplies of long-range precision-guided munitions might run dangerously low within days of a serious confrontation in the Taiwan Strait. While the U.S. Department of Defense (DoD) has never officially confirmed an “eight-day” depletion figure, congressional testimonies and partial government disclosures reinforce the central theme: replenishment rates for certain munitions lag behind likely wartime expenditure, leaving the United States potentially unprepared for a prolonged conflict.

Spurred by these concerns, this Concept Analysis Report draws on documents for guidance and data: aggregating information from DoD releases, congressional records, Chinese official statements, and think-tank simulations, and a structured analytical approach to map potential scenarios. By merging these sources, the report aims to clarify how industrial capacity, AI-driven warfare, alliance networks, and broader Chinese strategic goals might reshape the Indo-Pacific security landscape. Though these topics often evoke technical language—ranging from “precision-guided munitions” to “civil-military fusion”—they carry very real implications, both for the men and women in uniform and for the civilians whose lives would be disrupted by a major regional conflict. Throughout the report, references to war-game studies and official releases will be balanced by context and clarifications, in an effort to illuminate the reality behind high-profile yet sometimes unverified figures.

There's many different options, and none are "wrong", but many are different enough to make certain options "better" for specific uses. Knowing and understanding those uses and their associative advantages and disadvantages is very important to making the most of your utilization of the innovation hubs. I have direct links to many innovation hub web portals on my website. You can go to "the library" on my webpage, and click the "External References" button to see the massive listing to help start exploring all the innovation hubs that might be right for you.

Anxiety comes from lack of preparation. If you were going to play in a championship sporting event, you would prepare beyond the requirement of the event itself. You would workout, eat right, and train for the specific event over a sufficient period of time. Treat your pitches in military acquisition with the same level of commitment as you would for gameday. When you are adequately prepared you are excited to get out on the field to show what you can do. Anyone can see and sense that type of energy, and that's the type of person that garners confidence and trust, which ultimately builds belief and approval of the request.

Continuing my "Insider's Perspective" series, once you know what type of government funding you need, it's time to go out and find it. This is far easier said then done, but it all starts with mapping out the minefield of "NO's", to plot your path to "Yes". In the slides below are the important rules of thumb that will help guide you to a building a more successful strategy for those funding requests. The most common issue is spending far too much time working a funding request with an organization that could never even fulfill the request to begin with.

"Let's Talk Money", is the first in a series of DoD Acquisition related topics I will be covering in my next book. It's a popular topic because, far more often than not, both contractors and Gov/Military personnel are knowledgeable in some but definitely not all of the phases of Acquisition. This isn't about getting hung up the minutia of mapping one master process to rule all possible facets of DoD Acquisition, but rather highlighting the main points everyone must consider and have a plan to address. Regardless of your funding programs, contract choices, or customer organizations...everyone has to deal with these issues.

The Segment Anything Model 2 (SAM 2) represents a significant leap forward in computer vision and video understanding capabilities, with profound implications for military guidance and surveillance applications. This report provides a comprehensive assessment of SAM 2's potential military applications, focusing on its unique capabilities, likely impact on existing systems, and broader strategic implications.

Integrated the OpenAI “Stages of AI” levels into the chart to provide a more interesting and detailed projection to the timeline. As noted previously, the “level 2” threshold lines up exactly where OpenAI execs are mentioning in the press that GPT5 will be released. Also of note, if you extrapolate the expected score (GPQA diamond) of Claude 3.5 Opus, it to would fall right near the existing trend-line being released either late this year or early 2025.

I have number of defense industry friends who have called me up recently asking for solutions to the same hard problem. This was super useful for them, and since the DoD needs a better way to integrate capabilities holistically, I want to share this concept more broadly for applicable utilization. My friends were all looking for ways to rapidly integrate commercial capabilities with military customers, and the answer is surprising simple…plain text outputs.

Late last week Open AI internally released their “Stages of Artificial Intelligence”, and it’s worth digging into that a little to highlight the important achievements that are unlocked and stacked at each level.

Let me explain this in 3 simple points:1. It’s true in that most commercial products are not ready fit for any military customers, hence a new DefTech company will run into most of the core issues defined in the video.

So here’s the simple but EXTREMELY valuable process for drastically improving anything you do with LLMs. First, never zero-shot anything with your LLM usage unless it’s extremely simple tasks you have extreme confidence in the models capability of performing it. Always have the model review/edit its initial output. This is where all the value in the process below comes from. You want to instruct the model how to think, and not just rely on the inate capability of the model to produce the best answer for you. This is especially true the lower down the performance ladder you go with lesser models. In terms of reasoning, there are tons of frameworks and processes, and my current list has 105 across 21 categories. So which are best for what is the magic question to unlocking huge performance gains in output quality from any model.

Doing AI test question generation from source material is a relatively trivial task these days, but there is a hidden level of complexity to designing the best questions. I had worked this task originally as part of my time in service as part of Air Education & Training Command (AETC) back in 2023. I was able to create viable test questions from source content, but the argument at the time centered around the quality & conformity of the questions relative to existing formats. As the "how" of standardized test question formatting is a closely guarded secret in any training organization, I was working in the blind to all that (to ensure there wasn't any test compromise to existing content) through the duration of my work on the task. That said, I always liked the problem set, and have pondered often about better ways to solve it.

What LLMs do best is process existing documentation to produce new text within very complex interpretative instructions. Where that may be an ancillary part of many job workflows in the DoD, the single best fitting and widest impacting task that function represents is training content production. Every service hires very expensive highly experienced SMEs to support the creation, refinement, and publication of training content for every career field in the military. It is by far the most time consuming and labor intensive task for an expensive and limited talent pool. Training commands could leverage LLMs to both speed content creation as well as improve training content quality across all career fields.