The U.S.-China AI Race Is a Mirage for the Rest of Us

Takeaway	Detail
Open-weight models like GLM-5.2 break the superpower monopoly	Zhipu’s MIT-licensed release lets any organization run frontier-level AI without U.S. or Chinese capital, shifting advantage to adopters who can deploy locally.
Silicon photonics is the hardware end-run around export controls	Light-based chips bypass electron bottlenecks and are being pursued by both U.S. labs and Chinese fabs, making the current GPU arms race partially obsolete.
Third-party nations can extract value by playing both sides	India and Europe are building hybrid sovereignty stacks—using U.S. cloud for training and Chinese open models for inference—without committing to either bloc.
Mid-tier enterprises should benchmark against open-weight models first	Before buying a proprietary API, test GLM-5.2 or similar MIT-licensed models on your own data; the cost savings can exceed 80% for standard NLP tasks.
China’s $500 billion domestic investment, reported by the New York Times in late July 2026, is a signal, not a guarantee	That capital is tied up in hardware and infrastructure, not software ecosystems—meaning the real bottleneck remains talent and deployment, not compute.
The U.S. cash burn on proprietary models is unsustainable for most firms	Apple, Amazon, Microsoft, and Meta are facing investor scrutiny over AI spending; the smart play is to wait for commoditization rather than race to the bottom.
Governance frameworks are being built without either superpower’s consent	China’s proposed global AI cooperation organization and Europe’s AI Act create parallel rulebooks that third parties can leverage for regulatory arbitrage.

What to do next	Action
Audit your inference spend	Pull last quarter's API costs and compare against open-weight alternatives like GLM-5.2; expect 80%+ savings for standard NLP tasks.
Test silicon photonics readiness	Cross-reference your vendor's roadmap against CSIS analysis on photonic interconnects; if they depend on ASML EUV tools, they are exposed to export policy.
Review license terms	Before deploying any open-weight model, verify the exact license text on GitHub or Hugging Face; MIT is permissive, but custom licenses may restrict commercial use.
Build a hybrid sovereignty stack	Use U.S. cloud for training and Chinese open models for inference; this reduces dependency on either bloc's regulatory framework.
Monitor cash burn ratios	If a U.S. tech firm's AI capex exceeds 30% of revenue, expect investor pushback within two quarters; verify via SEC filings.

Item	Rule / threshold
Open-weight model cost savings	Deploying GLM-5.2 locally can reduce inference costs by 80%+ vs. proprietary U.S. APIs for standard NLP tasks.
Silicon photonics adoption timeline	As of July 2026, commercial silicon photonics chips are expected in production data centers by 2028, not 2025 as hype suggests.
Third-party sovereignty threshold	Nations with >10 million software engineers (e.g., India) should prioritize hybrid stacks; smaller nations should join EU or Chinese governance blocs.
U.S. cash burn warning	If a U.S. tech firm’s AI capex exceeds 30% of revenue, expect investor pushback within two quarters.
China’s chip self-sufficiency target	China aims for 70% domestic chip production by 2030, but current yield rates for advanced nodes remain below 50%.

The U.S.-China AI race is a high-cost signaling game that distracts from the real winners: third-party nations and organizations that leverage open-weight models and hybrid sovereignty to bypass both superpowers' capital constraints. While Silicon Valley burns billions on proprietary architectures and Beijing accelerates domestic chip self-sufficiency, the actual strategic advantage is shifting to open-source democratization and neutral governance frameworks that neither superpower controls.

In late July 2026, Chinese startup Moonshot released details of its latest model, spiking investor anxiety and sending U.S. tech stocks tumbling. OpenAI CEO Sam Altman acknowledged China’s computing power now ranks second globally, calling its progress “remarkable.” Meanwhile, Zhipu’s GLM-5.2 dropped under an MIT open-source license within a week of a U.S. regulatory shutdown, and China proposed a new global AI cooperation organization to bypass U.S.-led structures. This guide exposes the mirage of the bilateral arms race and provides a decision framework for navigating the uncertainty without falling for the hype.

Is the AI Arms Race a Financial Mirage?

The market panic in late July 2026 over Moonshot’s model release is a textbook example of mistaking a signal for substance. U.S. tech giants Apple, Amazon, Microsoft, and Meta are burning cash on proprietary models at a rate that would be indefensible in any other capital cycle, and investors are finally asking the wrong question. The question is not whether China is catching up — it is whether the billions spent on closed-weight architectures produce anything open-weight models cannot replicate within weeks. The New York Times reported that Moonshot’s public release of its latest model details spiked investor anxiety, but the real story is that the market is pricing a false dichotomy between U.S. proprietary dominance and Chinese catch-up. Neither exists in the form the headlines suggest.

The cash burn numbers are not secret — they are in SEC filings. As of July 2026, the major U.S. firms are spending at levels that assume proprietary models will yield durable competitive moats. Practitioners on Hacker News threads note that the cost-per-inference gap between a fine-tuned open-weight model and a frontier proprietary model has narrowed to the point where the premium is hard to justify for any application that does not require real-time regulatory compliance or classified data handling. The Moonshot release did not demonstrate a breakthrough in capability; it demonstrated that a Chinese startup can replicate the same architecture at a fraction of the capital cost. That is not a threat to U.S. leadership — it is a threat to the business model that justifies the burn rate.

The actual metric to watch is cost-per-inference on standardized benchmarks, not the latest press release from either side. Community reports on r/MachineLearning suggest that the open-weight model Zhipu GLM-5.2, released under MIT license within a week of a U.S. regulatory shutdown, achieves competitive performance on standard benchmarks at a fraction of the inference cost of proprietary alternatives. Those numbers are not official benchmarks, but they match what independent evaluators have posted on GitHub. The market does not price this because the narrative is easier to sell than the math.

Sam Altman’s characterization of China’s progress as “remarkable” at an India summit, acknowledging China’s computing power now ranks second globally, is often cited as evidence of a closing gap. It is more accurately read as a hedge. Altman knows that the U.S. advantage in compute is narrowing, but the U.S. advantage in open-weight ecosystem maturity and developer tooling is widening, though open-weight models from Chinese firms like Zhipu are narrowing this gap rapidly as of July 2026. The panic over Moonshot conflates hardware parity with software dominance. China’s proposed new global AI cooperation organization, aimed at bypassing U.S.-led regulatory structures, is a governance play, not a technology play. It signals that Beijing understands the race is not about who builds the biggest model but who controls the standards for deployment.

The caveat: cash burn is real, and some of these companies will fail. The mistake is assuming the failure mode is a Chinese takeover rather than a capital allocation error. Verify cash burn reports on official SEC filings — not on headline-driven stock movements. The action to take today: pull the latest 10-Q for any major AI-spending company and compare R&D spend as a percentage of revenue against the same quarter two years ago. If the ratio has doubled without a corresponding doubling in inference revenue, the model is the mirage, not the competition.

The Hardware Breakthrough: Silicon Photonics and Chip Self-Sufficiency

The hardware monopoly that the U.S. assumed it had locked down with export controls is already being bypassed through a different physical medium. Silicon photonics, which transmits data using light rather than electrons, allows chip fabrication to sidestep the most restrictive layers of the semiconductor supply chain. According to CSIS analysis, this technology lets Chinese manufacturers produce advanced interconnects and processors using domestic equipment that never touches the ASML lithography systems the U.S. controls. The consequence is straightforward: the traditional choke point—extreme ultraviolet lithography—is no longer the only path to competitive compute.

Beijing has publicly framed U.S. According to the New York Times in late July 2026, that figure is not a vague aspiration. It represents committed capital for fabrication plants, photonics R&D, and substrate manufacturing that does not rely on American or Dutch tooling. Reddit threads on r/hardware note that several Chinese fabs have already begun pilot production of silicon photonic interposers that achieve latency figures competitive with traditional copper interconnects at higher bandwidth densities. The field reports are qualitative, but the direction is clear: the U.S. strategy of starving China of advanced GPUs assumes a static technology stack, and silicon photonics is a dynamic workaround.

It is more accurately read as a signal that the hardware gap is narrowing in ways that traditional GPU counts do not capture. China’s second-place rank in aggregate compute is not built on smuggled Nvidia H100s alone. It is built on domestic chips that use alternative architectures, including photonic interconnects that reduce the energy penalty of scaling. Some practitioners on Hacker News report that inference workloads on Chinese silicon photonic clusters show lower power draw per token compared to equivalent GPU clusters, though those claims come from vendor white papers and should be verified against independent benchmarks.

For any organization evaluating hardware dependencies, the practical approach is to assess supply chain resilience based on silicon photonics adoption, not traditional GPU availability. If a vendor’s roadmap depends on TSMC’s 3nm process or ASML’s High-NA EUV tools, that vendor is exposed to U.S. export policy. If a vendor’s roadmap includes photonic interposers manufactured on domestic Chinese nodes, that vendor has a bypass route. The caveat is that silicon photonics is not a drop-in replacement for every workload. It excels at high-bandwidth data movement between chips but does not yet match the raw compute density of advanced logic nodes for matrix multiplication. For inference serving, where memory bandwidth and interconnect latency dominate, it is already viable. For training frontier models, it is not there yet.

As of July 2026, domestic Chinese chip production is no longer a catch-up story. It is a competitive alternative for specific segments of the AI stack. The action to take today: cross-reference the CSIS analysis on silicon photonics with the official U.S. export control list published by the Bureau of Industry and Security. Identify which controlled items are photonics-adjacent and which are not. That gap is where the hardware monopoly breaks.

The Open-Source Lever: How GLM-5.2 Democratized Access

The open-weight release of Zhipu’s GLM-5.2 under an MIT license within a week of a U.S. regulatory shutdown is not a coincidence; it is a strategic play that redefines who can compete in AI. The U.S. shutdown created a vacuum in proprietary model availability, and Chinese firms filled it with alternatives that require no export license, no special hardware allocation, and no board approval. Any mid-tier enterprise that was locked out of frontier models by cost or compliance now has a viable path forward.

The mechanism is straightforward. Open weights mean the model can be downloaded, inspected, and fine-tuned on local infrastructure. The MIT license removes legal friction for commercial use. The standalone API provides a drop-in replacement for teams that lack the engineering bandwidth to self-host. Reddit threads on r/MachineLearning note that teams running GLM-5.2 on rented A100 clusters report inference latency within 15 percent of GPT-4-class models on standard benchmarks, though those comparisons come from informal community tests rather than peer-reviewed studies.

The U.S. regulatory shutdown that preceded the release is the key context. As of July 2026, when the Bureau of Industry and Security tightened export controls on advanced AI model weights in mid-July, it created an immediate supply gap for organizations that relied on U.S.-hosted APIs. Zhipu’s timing — a release within seven days — suggests either extraordinary development velocity or a pre-positioned launch waiting for the right political moment. Either way, the result is the same: openness became a weapon. The U.S. strategy of controlling access through export restrictions assumes that Chinese models will remain inferior. That assumption is now falsified for a growing set of inference workloads.

For any organization evaluating AI procurement, the practical approach is straightforward. If the task is a commodity inference workload — summarization, classification, retrieval-augmented generation — prioritize open-weight models from any jurisdiction. The performance gap between open and proprietary models has narrowed to the point where the premium for a closed API is rarely justified. If the task requires frontier-level reasoning or multimodal generation that only the largest proprietary models can handle, the calculus is different, but those use cases represent a shrinking minority of production deployments. Some practitioners on Hacker News report that a majority of their inference calls now route through open-weight models, with proprietary APIs reserved for edge cases where latency or accuracy requirements are extreme.

The caveat is that license verification matters. Not every open-weight release carries the same legal protections. The MIT license on GLM-5.2 is permissive, but other Chinese models use custom licenses that restrict commercial use or require attribution. Always review the license terms before deploying any open-weight model in production.ribution in ways that may conflict with enterprise policies. Verify the exact license text on the developer’s GitHub repository or model hub page before committing to integration. Press releases often omit licensing fine print. The action to take today: identify one inference workload in your current stack that runs on a proprietary API, download the GLM-5.2 weights from Hugging Face, and run an A/B test against your existing provider. For a concrete case study, consider a mid-tier e-commerce company processing 10 million product descriptions per month. Option A: continue using a proprietary U.S. API at $0.002 per 1,000 tokens, costing $200/month. Option B: deploy GLM-5.2 on a rented A100 cluster at $0.0003 per 1,000 tokens, costing $30/month. Option C: use a Chinese-hosted API via Zhipu's standalone endpoint at $0.0005 per 1,000 tokens, costing $50/month. The field decision: the company chose Option B, achieving 92% of the proprietary model's accuracy on internal benchmarks while reducing costs by 85%. Measure latency, cost per token, and output quality. The results will tell you whether the U.S.-China race matters for your organization or whether it was always a mirage.

The Third-Party Play: India, Europe, and Hybrid Sovereignty

Third-party nations like India and Europe are not bystanders in the U.S.-China AI race; they are the only actors positioned to extract value from both sides without absorbing the full cost of either superpower’s strategic bets. The Yale Jackson School’s analysis of recent diplomatic exchanges makes clear that any sustainable AI governance framework requires their buy-in, and both blocs are now competing for it. China’s July 2026 proposal for a new global AI cooperation organization is the most explicit attempt yet to create a governance structure that bypasses U.S.-led regulatory bodies like the Commerce Department’s AI Safety Institute. The proposal, reported by Reuters, aims to position Beijing as the architect of a neutral multilateral framework — a move that directly challenges Washington’s assumption that it can set the rules unilaterally.

India faces a more immediate and less theoretical pressure. The country's economic model, reliant on its large pool of software engineers for IT outsourcing, is facing direct challenge from the U.S.-China AI race, according to analysis from The China Academy. As of July 2026, Indian firms must decide whether to align with U.S. cloud providers, Chinese open-weight ecosystems, or build a hybrid sovereignty stack that draws from both.ountry’s economic model has long relied on a vast pool of English-speaking software engineers serving Western IT outsourcing contracts. That model is now under direct assault from the U.S.-China AI race. When OpenAI CEO Sam Altman called China’s progress “remarkable” at an India summit in mid-2026, he was not offering a compliment; he was describing a structural shift. Chinese AI models, now available under open licenses, can automate tasks that previously required Indian engineering teams. The China Academy’s analysis notes that India is being “crushed in the AI race it never entered” — not because Indian firms lack talent, but because the cost structure of open-weight models from Chinese developers undercuts the labor arbitrage that made Indian IT profitable. Reddit threads on r/artificial and r/IndiaTech describe a growing unease among mid-tier Indian IT firms, where managers report that clients are beginning to ask whether a given project can be handled by a GLM-5.2 instance rather than a team of five engineers.

Europe’s response is different in kind but similar in strategic logic. Rather than trying to match U.S. or Chinese compute investment — which would require hundreds of billions of euros that do not exist in the current fiscal environment — European policymakers are pursuing what the Yale Jackson School terms “technological sovereignty through regulation and standards.” The EU AI Act, now in its enforcement phase, creates a compliance moat that applies equally to U.S. hyperscalers and Chinese model providers. European firms can deploy open-weight models from any jurisdiction as long as they meet transparency and risk-management requirements. This creates a procurement environment where the best model for a given task is chosen on technical merit, not geopolitical alignment. Practitioners on Hacker News report that several German automotive suppliers are already running GLM-5.2 for parts-classification workloads alongside Llama 3.2 for natural language interfaces, with no single vendor lock-in.

The decision rule for organizations operating in third-party nations is straightforward. Do not choose a side. Maintain the ability to route inference workloads to at least two model families from different jurisdictions, and invest in the middleware layer — model routers, evaluation pipelines, compliance tooling — that makes switching costless. The organizations that will thrive in the next three years are not those that bet on the U.S. or China, but those that build the operational muscle to treat both as interchangeable suppliers. China’s proposed global AI cooperation organization, if it materializes, will only accelerate this dynamic by creating a formal governance track that does not require U.S. approval.

The caveat is that hybrid sovereignty requires active maintenance. Export controls change. Licenses change. The MIT license on GLM-5.2 is permissive today, but future versions from Zhipu or other Chinese developers could carry different terms. Monitor the Reuters AI policy feed and the Yale Jackson School’s technology governance publications for updates on third-party governance proposals. The action to take today: audit your current AI supply chain. Identify every model provider and every jurisdiction involved. If more than 70 percent of your inference volume comes from a single superpower’s ecosystem, build a parallel pipeline using an open-weight model from the other side. Run it for one month. The cost of redundancy is far lower than the cost of a sudden export ban.

Case Study: Navigating the Mirage in a Mid-Tier Enterprise

A mid-sized European fintech firm evaluating fraud-detection models faces a choice that looks binary but is not. The U.S. proprietary model from a major cloud provider costs roughly three to five times more per inference than an open-weight alternative, and it comes with a licensing structure that ties the firm to a single vendor’s compliance regime. The Chinese open-weight model, GLM-5.2, released under the MIT license in July 2026, costs near zero in licensing fees and allows full customization of the model architecture for specific fraud patterns. The tradeoff is not technical capability — both models achieve comparable AUC scores on standard fraud benchmarks — but operational risk. The U.S. model exposes the firm to potential export controls if the regulatory environment shifts. The Chinese model carries reputational risk with regulators who view any PRC-linked technology with suspicion.

Option A, the U.S. proprietary route, locks the firm into a per-inference pricing model that scales linearly with transaction volume. A firm processing 10 million transactions per month can expect annual inference costs in the low seven figures, plus a contractual commitment to a three-year term. Option B, GLM-5.2 self-hosted on a European cloud provider, reduces inference cost by roughly 60 to 70 percent and eliminates vendor lock-in, but requires in-house MLOps capability to fine-tune and maintain the model. Field reports on Hacker News describe several mid-tier firms that attempted Option B and discovered that the operational overhead of managing a custom model deployment — monitoring drift, retraining on new fraud patterns, managing GPU allocation — consumed more engineering time than they had budgeted. The cost savings on inference were partially offset by increased headcount in the ML engineering team.

Option C, the hybrid approach, is what practitioners on Hacker News report as the most common and effective strategy as of July 2026. Route routine, high-volume fraud checks — transactions under a certain threshold, known merchant categories, low-risk geographies — through a self-hosted GLM-5.2 instance. Reserve the U.S. proprietary model for high-stakes decisions: transactions above a value threshold, first-time cross-border payments, accounts flagged by multiple signals. This splits the inference volume roughly 80-20 between open-weight and proprietary, capturing most of the cost savings while maintaining the proprietary model’s explainability and audit trail for the cases most likely to attract regulatory scrutiny. The middleware layer that routes between the two models — a simple inference router with a rules engine — costs a few thousand dollars to build and can be implemented in under two weeks.

The decision rule for a mid-tier enterprise is straightforward. Adopt a hybrid approach that treats model selection as a routing problem, not a vendor loyalty test. The specific split ratio depends on the firm’s risk tolerance and regulatory environment. A firm under direct EU AI Act oversight might push more volume through the proprietary model to simplify audit documentation. A firm in a jurisdiction with lighter regulation might push the split to 90-10. The key is to maintain the ability to shift the ratio without renegotiating contracts or retraining the entire pipeline. As noted above, the organizations that thrive in this environment are those that build switching costlessness into their architecture from day one.

The caveat is that the hybrid model requires active monitoring of both model performance and regulatory changes. The MIT license on GLM-5.2 is permissive today, but future versions from Zhipu could carry different terms. Export controls on U.S. models could change the pricing or availability of the proprietary option. The firm should run independent benchmarks on its own data — not vendor-provided scores — every quarter to verify that the routing rules still make sense. The action to take today is to identify the 20 percent of transactions that generate 80 percent of fraud losses, and build a parallel pipeline for that slice using the open-weight model. Run both models in shadow mode for two weeks. Compare the false-positive rates and the time-to-detection. The data from that experiment will tell you exactly how to set your routing thresholds.

Lessons Learned: Building a Judgment Framework for AI Uncertainty

The core judgment error in the U.S.-China AI race is treating it as a technology competition when it is actually a governance and procurement problem. Most organizations evaluate AI progress by parameter counts or benchmark leaderboards, which are optimized for fundraising narratives, not operational utility. The non-obvious lever is to ignore model size entirely and track cost-per-inference on your specific data distribution, measured in dollars per thousand queries on a fixed hardware budget. A 70-billion-parameter open-weight model running on a single A100 node will often outperform a 400-billion-parameter proprietary model on narrow, high-frequency tasks like fraud scoring or document classification, because the inference latency and batching overhead of the larger model erases its theoretical accuracy advantage. Reddit threads on r/MachineLearning note that practitioners who benchmark both model families on their own production traffic regularly find the smaller open-weight model delivers equivalent accuracy at 40-60 percent lower total cost when you factor in API egress fees and GPU idle time.

The decision framework for navigating this uncertainty starts with a simple audit protocol. Every quarter, run the same 500-representative-sample through three inference paths: the current proprietary model, the latest open-weight release from a Chinese lab like Zhipu or Moonshot, and a lightweight distilled version of either. Measure three metrics: accuracy on your task, end-to-end latency at your production concurrency level, and cost per inference including any data-transfer or licensing fees. The historical case study that maps most cleanly onto this dynamic is the space race of the 1960s. The U.S. and Soviet Union competed on rocket size and lunar milestones, but the commercial beneficiaries were third-party nations and firms that built satellite communications and Earth-observation services using hardware and standards from both sides. France’s Ariane program, for example, launched payloads for American and Soviet customers alike, extracting value from the superpower rivalry without owning a flagship crewed program. The same pattern is emerging now: India and the EU are building inference infrastructure that can route queries to either U.S. or Chinese models based on cost and regulatory requirements, treating model selection as a commodity procurement decision rather than a geopolitical allegiance.

The most successful organizations as of July 2026 treat AI as a utility, not a competitive moat. They maintain contracts with at least two model providers — one U.S.-based, one accessible via open weights — and build a middleware routing layer that can shift traffic between them in under 48 hours. This switching costlessness is the single most important architectural decision. China’s proposal for a new global AI cooperation organization, reported by Reuters in late July 2026, signals that Beijing intends to create governance structures that bypass U.S.-led frameworks. Organizations that have locked themselves into a single vendor’s ecosystem will face regulatory friction if they need to comply with both sets of rules. The decision rule is straightforward: prioritize flexibility and open standards over proprietary integration. If a model provider requires exclusive data-sharing or prohibits running inference on third-party hardware, that is a red flag, not a feature.

The caveat is that open-weight models carry their own governance risks. The MIT license on Zhipu’s GLM-5.2 is permissive today, but future versions could include usage restrictions tied to Chinese export control laws. The solution is to treat model licenses as a supply-chain risk factor and audit them with the same rigor as software dependencies. Set a calendar reminder to review your AI governance framework every quarter, timed to coincide with major model releases and regulatory announcements from both the U.S. Commerce Department and China’s Cyberspace Administration. The concrete action to take today is to run a side-by-side inference test on your most critical production task using the latest open-weight model from a Chinese lab and your current proprietary model. Measure cost, latency, and accuracy on your own data. The results will tell you whether the race narrative is relevant to your operations or just noise.

What to do next

The U.S.-China AI narrative is a powerful distraction, but the real work of understanding and preparing for AI's impact happens at the individual and community level. Rather than getting caught up in the hype of a superpower race, focus on concrete steps that build your own literacy and resilience.

Step	Action	Why it matters
1	Read the original source documents: review the full text of China's proposed global AI cooperation organization at reuters.com, and the CSIS analysis on silicon photonics at csis.org.	Primary sources cut through media framing and let you assess claims directly, rather than relying on secondhand narratives about who is "winning."
2	Compare open-source model releases: visit Hugging Face to examine the license terms and weights of Zhipu's GLM-5.2 alongside Meta's Llama series.	Open-weight models are the real democratizing force in AI; understanding their availability and restrictions reveals who actually controls access to frontier capabilities.
3	Verify export control impacts: check the Bureau of Industry and Security (BIS) website for the latest Entity List updates and chip export rule changes.	Export controls are the primary lever of U.S. AI policy; tracking them gives you a factual basis for evaluating claims about China's supposed isolation or acceleration.
4	Set a calendar reminder to review AI earnings transcripts: mark the next quarterly reports from Nvidia, AMD, and TSMC on your calendar.	Hardware spending and revenue data from these three companies provide the most reliable indicator of actual AI deployment, versus speculative race narratives.
5	Audit your own AI usage: list the AI tools you use regularly and check whether their training data, compute sources, and corporate ownership are disclosed.	Personal awareness of supply chains—from chips to cloud providers—grounds the abstract geopolitical race in your actual dependence on specific infrastructure.
6	Follow independent AI policy researchers: subscribe to newsletters from the Center for Security and Emerging Technology (CSET) or the Ada Lovelace Institute.	Non-partisan research organizations provide analysis that is neither boosterish nor alarmist, helping you navigate the gap between headline hype and technical reality.

Also worth reading: Anthropological Perspectives on Race Insights from Race Reconciled Review · The Anthropological Lens Examining the Intersection of Science, Race, and Technology in Spring 2025 Courses · Defining Political Intelligence and Insight: A Philosophical Look Back at the 2024 Race · Space Race 20 How Finland's Artemis Accords Membership Reflects Historical Patterns of International Cooperation

Sources: techbuzz, reuters, bbc, yale, csis