The “Artificial Intelligence Manhattan Project”

The United States is in a high-stakes race to achieve cognitive dominance – superior AI capabilities that confer strategic advantage in military, intelligence, and economic domains. American officials now openly liken the AI competition to a new Manhattan Project in scale and urgency. As Energy Secretary Chris Wright declared in 2025, “The global race for AI dominance is the next Manhattan Project, and … the United States can and will win” (US plans to develop AI projects on Energy Department lands | Reuters). This report surveys major U.S. public and private AI initiatives with Manhattan Project-like ambition, examines strategic dynamics such as Mutual Assured AI Malfunction (MAIM) deterrence, and considers whether America is already running a de facto “AI Manhattan Project.” It also highlights expert warnings – including from the Superintelligence Strategy paper by Hendrycks, Schmidt, and Wang – about the risks of an unchecked arms race and loss of control.

U.S. Government-Led AI Initiatives on a ‘Manhattan Project’ Scale

American government agencies have launched numerous AI programs approaching Manhattan Project scale in resources and strategic importance. While not centralized under one lab as in the 1940s, these efforts span defense, intelligence, energy, and more, collectively mobilizing billions of dollars and top talent. Key government-led initiatives include:

• DARPA’s “AI Next” Campaign (Third Wave AI) – The Defense Advanced Research Projects Agency (DARPA) has a storied role in AI development since the 1960s (DARPA Announces $2 Billion Campaign to Develop Next Wave of AI Technologies). In 2018 DARPA announced a multi-year $2 billion initiative called the AI Next campaign to drive a “third wave” of AI research beyond today’s machine learning. This large-scale effort focuses on contextual reasoning and adaptable AI that can partner with humans, addressing the brittleness of current systems. Under AI Next, DARPA is running over 20 programs pushing the state-of-the-art (from explainable AI to autonomous systems), plus dozens of applied projects for the military. While fragmented across many research teams, the funding and ambition echo a Manhattan-like push for breakthroughs.

• Department of Defense Joint AI Efforts (JAIC/CDAO) – The Pentagon created the Joint Artificial Intelligence Center (JAIC) in 2018 as a central hub to “transform the DoD by accelerating the delivery and adoption of AI” across the armed services (Joint Artificial Intelligence Center - Wikipedia). With direct backing from the Secretary of Defense, JAIC’s mandate was to apply AI at scale to “large and complex problem sets” in combat and operations. It coordinated projects ranging from predictive maintenance and battlefield intelligence to autonomous vehicles. In 2022, JAIC was elevated and merged into the new Chief Digital and AI Office (CDAO) to further institutionalize AI integration. This reflects a concerted, top-down drive to infuse AI into U.S. military systems – essentially bringing the nation’s vast defense apparatus into the AI era. The DoD is also investing in specific “AI super-weapon” concepts, for example testing AI copilots in fighter jets and autonomous drone swarms, under projects like DARPA’s ACE (Air Combat Evolution). Such efforts, while public, carry a flavor of Manhattan Project urgency in their bid to maintain U.S. battlefield superiority through AI.

• Department of Energy Supercomputing & AI Infrastructure – The Department of Energy (DOE) is leveraging its national labs and supercomputing might to advance American AI on an infrastructural level. DOE labs like Oak Ridge (a Manhattan Project birthplace) now host the world’s top supercomputers (e.g. Frontier and Summit) explicitly used to train cutting-edge AI models (How one national lab is getting its supercomputers ready for the AI age | FedScoop) (How one national lab is getting its supercomputers ready for the AI age | FedScoop). Frontier, the first exascale computer, can perform >10^18 operations per second and is “capable of, collectively, training some of the largest AI models known”. This compute power is a strategic asset – akin to the nuclear material of the AI era – and the U.S. is pouring funding into more. In fact, the U.S. government has begun siting AI-dedicated data centers on DOE lands. In 2025, 16 federal sites (including former nuclear facilities) were earmarked for rapid construction of data centers and even small nuclear reactors to power AI research. This plan, endorsed by DOE leadership, treats computing infrastructure as the backbone of AI dominance, much as enrichment plants were for the atomic bomb. By fast-tracking massive compute and energy for AI, DOE is providing a Manhattan Project–like foundation (in hardware and energy) for American AI efforts.

• Intelligence Community AI Programs – Within the classified realm, U.S. intelligence agencies are aggressively adopting AI, though specific “superintelligent” projects remain secret. The National Security Agency (NSA), with an annual budget reportedly over $10 billion, describes itself as the “leader among U.S. intelligence agencies racing to develop and deploy AI” (How is One of America's Biggest Spy Agencies Using AI? We're Suing to Find Out. | ACLU). NSA uses AI for signals intelligence, cybersecurity threat hunting, and automating surveillance analysis at unprecedented scale. Meanwhile the CIA is building its own ChatGPT-style generative AI tools for analysts, aiming to “scour the entire public web, condense information and summarize it” to augment intelligence gathering (CIA Plans Its Own ChatGPT-Style Tool to Help 'Find the Needles') (The CIA says it's building a ChatGPT-style generative AI chatbot to ...). These known efforts hint at deeper classified projects. It is widely assumed that covert AI R&D is underway in programs shielded from public view – potentially including attempts to develop AI systems for codebreaking, strategic forecasting, or even forms of artificial general intelligence for national security. The NSA has run internal AI research for years (the agency completed multiple studies and roadmaps on AI impacts and likely maintains secret contracts with leading AI companies or defense contractors. While concrete details are sparse, the scale of U.S. intelligence interest and funding for AI suggests Manhattan-level clandestine efforts “behind closed doors” to ensure the U.S. maintains an edge in AI-enabled espionage and warfare.

Corporate-Led “AI Manhattan” Initiatives in the U.S.

The private sector is driving much of America’s cognitive dominance race, with a handful of tech companies pursuing “Manhattan Project”-scale AI endeavors. Unlike the original Manhattan Project (a single top-secret program), today’s AI race is spearheaded by corporate labs – often in partnership with government – that are pouring enormous resources into developing advanced AI, including the elusive goal of Artificial General Intelligence (AGI). Major U.S. corporate-led initiatives include:

• OpenAI (with Microsoft) – OpenAI is arguably the closest analog to a Manhattan Project in the private sector. Founded with a mission to create “safe AGI”, OpenAI transitioned from nonprofit to a capped-profit model and secured unprecedented funding from Microsoft (over $10 billion committed). It built the famous GPT series of large language models, culminating in GPT-4 (2023) which demonstrated human-level performance on many tasks. OpenAI’s work is explicitly Manhattan-like in scale: training GPT-4 consumed thousands of Nvidia A100 GPUs over months, and future models will require even more. The company has lobbied Washington for “Apollo program level” support for AI, even presenting an “Infrastructure Blueprint for the US” that cites the Manhattan Project as an iconic model for bold national investment (US Commission Calls for 'Manhattan Project' to Beat China in AI Race - Business Insider). OpenAI warns that America’s lead in AI “is not wide and is narrowing” (Dan Hendrycks warns America against launching a Manhattan Project for AI), urging a massive acceleration. Internally, OpenAI is reportedly working toward GPT-5 or other next-gen systems, with speculations of training runs requiring 10^25 FLOPs (floating-point ops) – on the order of tens of thousands of top-end GPUs (Anthropic's $5B, 4-year plan to take on OpenAI | TechCrunch). This is a computing effort on par with large government projects. In effect, OpenAI (buttressed by Microsoft’s cloud infrastructure and cash) has become a de facto Manhattan Project for AGI on U.S. soil, albeit one that is corporate-led and semi-public.

• Anthropic – Anthropic is another U.S. AI lab with Manhattan-scale ambitions. Founded in 2021 by ex-OpenAI researchers, Anthropic positions itself to “build reliable, interpretable, and steerable AI systems” with a long-term aim of safe AGI. The startup has raised enormous capital (over $1.5 billion by 2023, including a $4B commitment from Amazon) and laid out plans to spend $5 billion in the next few years to challenge the leaders (Anthropic's $5B, 4-year plan to take on OpenAI | TechCrunch). According to a leaked investor deck, Anthropic is developing a “frontier model” called Claude-Next that they estimate will be 10× more capable than today’s strongest models (Anthropic's $5B, 4-year plan to take on OpenAI | TechCrunch). Achieving this requires on the order of 10^25 FLOPs of computation – several magnitudes above current norms – and roughly $1 billion in training costs over 18 months (Anthropic's $5B, 4-year plan to take on OpenAI | TechCrunch). These figures mirror Manhattan Project levels of expense (billions of dollars) and focus (a singular goal of a breakthrough system). Anthropic’s work on “constitutional AI” (an approach to align AI with human values) also underscores the recognition of control and safety as paramount, even as they aggressively scale models. With backing from big tech firms and a clear mandate to push the frontier, Anthropic represents the startup equivalent of a Manhattan Project, racing to outrun larger rivals while maintaining safety guardrails.

• Google DeepMind – Google has consolidated its AI might by merging DeepMind (the London-based pioneer behind AlphaGo) with its Google Brain team, forming a single unit (Google DeepMind) dedicated to advanced AI. DeepMind’s leadership has openly aimed for AGI for years, and the merged entity has unparalleled resources. Housed within Alphabet, Google DeepMind can draw on Google’s vast computing infrastructure (likely millions of CPU/GPUs across data centers) and talent pool. Notably, DeepMind’s research environment was often compared to “Los Alamos for AI” – a concentration of world-class researchers (Demis Hassabis, Shane Legg, etc.) working somewhat insulated from commercial pressures. Now under CEO Demis Hassabis, Google DeepMind is reportedly developing a next-generation model known as “Gemini”, intended to leapfrog OpenAI’s GPT-4 by incorporating advanced reasoning (combining techniques from DeepMind’s AlphaGo-type systems with large language models). While details are proprietary, the scale is tremendous: training runs on TPU v4 pods (Google’s supercomputers) and likely involving trillions of parameters. Google’s strategy blends pure research with deployment – e.g. integrating powerful models into products like Search, or medical and robotics applications – but with an explicit eye toward safe AGI. In 2023, DeepMind released a 140-page paper on technical AGI safety, acknowledging AGI could arrive this decade and “cause severe harm” if not properly controlled (Google DeepMind 145-page paper predicts AGI will match human ...). This mix of urgency to innovate and concern for control captures the Manhattan Project ethos within Google: they are racing to build transformative AI before competitors, while trying to avoid an AI catastrophe. Google’s effort is perhaps the most resource-rich AI project on Earth, essentially a corporate Manhattan Project spanning multiple continents (with research hubs in the US, UK, Canada, etc.).

• Meta AI (Facebook) – Meta Platforms is also a significant player, though pursuing a different approach by open-sourcing much of its AI research. Meta’s AI labs (FAIR) have developed large language models like LLaMA (with 70 billion parameters) and released them openly to researchers. In 2023, Meta, in partnership with Microsoft, released LLaMA 2 as a free commercial model, positioning open innovation as a strategic counter to more closed ecosystems. While Meta’s scale (in terms of trained model size and compute) is comparable to peers, it frames its work more as an open infrastructure for AI rather than a race to a secret weapon. Still, Meta is investing billions in AI research and hardware (for example, building new AI-focused data centers and custom chips), recognizing that AI capabilities are critical to its future products (the metaverse, content curation, ads, etc.). One could say Meta is running a “Manhattan Project for open AI” – trying to ensure the wider AI community (and by extension the US, where Meta is based) has broad access to advanced AI, preventing any one actor from monopolizing the cognitive high ground. Meta’s contribution to U.S. cognitive dominance is indirect but important: by open-sourcing powerful models, it potentially denies adversaries an easy win and keeps innovation momentum within an American-led sphere. However, the very openness of Meta’s approach contrasts with the secrecy of a true Manhattan Project, illustrating the diverse philosophies in the AI race.

Other U.S. companies also play roles (e.g. Microsoft, beyond its OpenAI investment, is integrating AI across software and cloud; IBM is focusing on AI for enterprise and government with its watsonx platform; smaller startups like Inflection AI are building large models too). But the tip of the spear in the cognitive dominance race remains the trio of OpenAI, Anthropic, and Google DeepMind, with Meta as a significant contributor. These initiatives each command talent and budgets on the order of a major government project, and their goals – achieving human-level or superhuman AI – are as revolutionary as the goal of the Manhattan Project in its time.

Strategic Stakes: Deterrence, MAIM Dynamics, and Control Risks

The race for AI supremacy is not happening in a vacuum – it is drawing intense scrutiny from national security strategists, because an overwhelming lead in AI could translate to geopolitical dominance. Analysts Hendrycks, Schmidt, and Wang argue that advanced AI is now “the most precarious technological development since the nuclear bomb” (Superintelligence strategy.pdf). Accordingly, the U.S. and its rivals must navigate this race with the caution of a nuclear standoff. A central concept emerging is Mutual Assured AI Malfunction (MAIM), a deterrence dynamic analogous to Cold War Mutual Assured Destruction. Under MAIM, no major power can seize unilateral AI dominance without risking disastrous retaliation.

According to the Superintelligence Strategy paper, MAIM is already the de facto strategic regime among AI superpowers. In essence, “any state’s aggressive bid for unilateral AI dominance is met with preventive sabotage by rivals”. The barrier to entry for such sabotage is low: an adversary could deploy covert cyber operations to corrupt a competitor’s model training (for instance, subtly degrading an AI’s performance), or even launch kinetic strikes against critical data centers . Given the relative ease of disrupting an AI project – especially compared to locating and destroying mobile nuclear launchers – no nation can confidently sprint to superintelligence without looking over its shoulder. Any attempt to build a decisive super-AI advantage “would invite a debilitating response”, as rivals will not tolerate a situation where they risk total strategic inferiority or an out-of-control AI catastrophe. This creates a tense equilibrium: like nuclear MAD, everyone is deterred from going too far, too fast.

Importantly, MAIM encompasses not only deliberate attacks by rivals but also the risk that a racing country “inadvertently loses control of its AI”, causing a disaster that harms all. In the worst case, a misaligned superintelligence could inflict catastrophic damage (even “omnicide”) globally – a scenario so dire that other states would feel compelled to intervene preemptively. Thus, whether a nation is winning or losing the race, pushing the accelerator carries existential risks: if you succeed, your rivals may destroy your project or even go to war; if you fail and the AI escapes control, it could spell doom for everyone. This double-edged threat underpins the logic of MAIM deterrence.

Hendrycks, Schmidt, and Wang underline that MAIM, while more abstract than nuclear MAD, is becoming the default unless nations cooperatively restrain their AI ambitions. They note that just as the U.S. and USSR eventually accepted mutual vulnerability with nuclear arms, a “similar state of mutual strategic vulnerability looms in AI”. For stability, the authors suggest measures like clear signaling of red lines, hardening AI labs against attack, and transparency between powers to avoid miscalculations. One idea is to keep major AI computing centers away from population centers (to reduce the temptation of a decapitation strike that could kill civilians) (Superintelligence strategy.pdf). Another is negotiating compute monitoring regimes – akin to arms control for GPUs – so each side knows if the other is amassing the means to sprint ahead. These proposals show how deterrence logic is being adapted to AI: the aim is to prevent an AI arms race spiral that could lead to either a war or a world-ending AI accident.

From an American perspective, the strategic stakes are two-fold: the U.S. wants to win the race to safe, superior AI, but it must do so in a way that doesn’t trigger conflict or calamity. This is a delicate balance. On one hand, losing leadership in AI to an authoritarian rival (like China) is seen as unacceptable – it could tilt the global balance of power and undermine U.S. security and values. This fear is driving calls in Washington for Manhattan Project urgency to “beat China” to AGI (US Commission Calls for 'Manhattan Project' to Beat China in AI Race - Business Insider). A bipartisan commission in late 2024 explicitly warned that the U.S. needs a “Manhattan Project-like” program to ensure AGI leadership. On the other hand, racing too hard has its own perils. Many AI experts caution that an international “AGI race is a suicide race” if it leads to corner-cutting on safety. Yoshua Bengio and others worry that relentless competition increases the odds of creating a rogue AI or inciting violent confrontation. As Professor Max Tegmark noted, if each side treats AGI as a winner-takes-all prize, the outcome could be mutual destruction rather than victory.

The United States thus faces a strategic conundrum: how to stay ahead in AI without destabilizing the world. The emerging U.S. strategy, as evidenced by policy and the Hendrycks-Schmidt-Wang paper, has three pillars: deterrence, non-proliferation, and competitiveness (Superintelligence strategy.pdf). Deterrence (MAIM) discourages adversaries from reckless AI bids. Non-proliferation aims to curb the spread of dangerous AI capabilities (for example, the U.S. is restricting exports of top-tier AI chips to China and limiting investments in Chinese AI firms, an effort to slow a rival’s progress and prevent weaponizable AI proliferation). Competitiveness means investing in domestic AI R&D and talent so the U.S. maintains an edge – essentially an innovation race that hopefully stays short of a destructive arms race. If balanced well, this approach seeks to deter the worst outcomes (prevent a rival’s AI “first strike” or an AI accident) while vigorously pursuing beneficial AI.

Still, the risk of loss of control looms large. The U.S. military and labs may build extremely powerful AI systems in the coming years as part of this dominance quest. With power comes unpredictability – advanced AI might behave in unanticipated ways or be misused. A core lesson from the strategic analysis is that achieving cognitive dominance must go hand-in-hand with ensuring AI remains controllable. An out-of-control superintelligence is a nightmare scenario for every nation. Thus, even as American projects charge forward, there is parallel work on AI alignment and safety (for example, DARPA’s new programs on AI assurance, and safety research by OpenAI, DeepMind, Anthropic and others). Indeed, Eric Schmidt (former Google CEO and NSCAI chair) emphasizes building deterrents against misaligned super AI – essentially capabilities to detect and if necessary neutralize a rogue AI – as part of national strategy. This reflects a mindset of “trust but verify” – or more starkly, prepare to fight or sabotage AI if something goes awry, whether it’s an enemy’s system or one’s own that has gone rogue.

In summary, the strategic environment around AI is increasingly reminiscent of the early nuclear age: a mix of racing and restraining. America is striving for superiority in AI, yet recognizes that if anyone truly wins the race in an absolute sense, everyone could lose. This paradox defines the MAIM era. The U.S. is therefore pursuing AI dominance with a degree of caution, attempting to stay ahead but also to avoid lighting the fuse of an uncontrollable situation. Whether this tightrope act will hold is one of the defining security questions of our time.

Secret or “Manhattan-Scale” AI Programs: Rumors and Realities

Considering the enormous stakes, it is natural to ask: Does the United States have a secret AI Manhattan Project? During WWII, the Manhattan Project itself was ultra-secret – might there be a modern equivalent hidden from public view, where the U.S. government is covertly trying to leapfrog to superintelligent AI? There is intense speculation around this question. While no official confirmation exists, a few pieces of evidence and informed guesses can be discussed:

• Historical Precedent and Black Budget Resources: The U.S. has a history of undertaking large classified technology projects (from Cold War codebreaking efforts to stealth aircraft development and SIGINT programs like PRISM). AI, being potentially even more transformative, could justify a black-budget program on the order of billions of dollars annually. The Department of Defense and Intelligence Community have significant discretionary funds and compartments for Special Access Programs. It’s conceivable that a portion is allocated to a covert superintelligence initiative – for instance, a joint DARPA-NSA project to develop an AGI for cyber defense/offense, or a CIA program to harness AI for strategic analysis beyond public capabilities. Such a program would likely involve top AI talent with security clearances, operating under NDAs and not publishing research. Facilities at national labs or military research centers could be used, perhaps leveraging infrastructure like the DOE supercomputers but on isolated networks. The Energy Department’s overt moves (offering sites for AI data centers  hint that even more ambitious classified sites might be in the works, given many DOE labs have both open and classified sections. In short, if a “Project X” for AI exists, the U.S. government has the means to fund and conceal it in the maze of defense R&D spending.

• Arguments Against a Secret AGI Leap: Some experts are skeptical that a Manhattan Project-style secret leap in AI is possible – at least in the U.S. context. Modern AI progress is a globally distributed enterprise, with breakthroughs emerging from open research communities and companies. The government necessarily relies on private-sector AI contractors and academics for cutting-edge work, which makes keeping major advances secret difficult. As one analyst noted, “deep learning…requires a lot of talent and data that isn't easy for the government to acquire”, and any foundational techniques or models developed in a secret program would likely filter back to industry through the personnel and firms involved ([D] Is an AI "Manhattan Project" possible? : r/MachineLearning). Unlike the 1940s, when a small set of physicists could be sequestered at Los Alamos, today’s top AI minds are scattered across companies and universities, often publishing openly. It would be hard to corral enough of them into a closed project without the rest of the community noticing their absence or the telltale signs (e.g. massive GPU purchases). Additionally, the vast compute requirements of frontier AI mean a secret project would leave footprints (such as large power consumption or chip acquisitions) that are hard to hide completely. In other words, AI is not quite like the atom bomb in terms of secrecy – much of the “AI supply chain” (chips, data, algorithms) is in commercial hands, so a purely government-run, totally secret development is less feasible (An AI Manhattan Project is Not Inevitable — EA Forum). This perspective suggests that instead of one hidden Manhattan Project, the U.S. will continue to leverage the open industrial base and then insert secret sauce at the last steps (for example, fine-tuning models on classified data, or integrating them into secret military systems). In this view, the government can achieve technological superiority “without nationalizing the entire AI industry”.

• Informed Speculation: Within tech circles, there is buzz that something akin to a “Project Turing” or “Manhattan Project 2.0” might be underway quietly. For instance, rumors suggest the U.S. defense establishment might be testing large-scale AI models for intelligence that rival the best from OpenAI/Google but kept classified for now. It’s also possible that joint international efforts exist among U.S. allies (the UK, Canada, etc. – all of whom have top AI labs) to collaborate on safe AGI research in a semi-covert manner, sharing the costs and talent with strict secrecy. Another angle is public-private partnerships under NDA: for example, a major cloud provider or AI firm could have a secret contract to develop an AI system for government use that pushes the envelope beyond what is released publicly. If, say, the NSA contracted a company to train a custom model with unprecedented capabilities (using proprietary data or special architectures), the result might never be published or announced – effectively a shadow Manhattan Project running on a corporate cloud. One tangible hint is that the U.S. Air Force has openly discussed an “Autonomous Air Combat” initiative to yield AI pilots and advisors, which is partially public (DARPA’s ACE program) and partially classified as it moves to deployment. This hybrid approach – public research, secret application – could be how a modern Manhattan Project is structured.

• What the Experts Say: In their Superintelligence Strategy paper, Hendrycks, Schmidt, and Wang explicitly warn against the U.S. launching a full-blown government-run AGI Manhattan Project. They argue it would likely backfire by prompting severe counter-measures from other powers (Former Google CEO Eric Schmidt opposes US government-led push for smarter-than-human AI: 'The Manhattan Project assumes...' - The Times of India). “A Manhattan Project [for AGI] assumes that rivals will acquiesce to an enduring imbalance or omnicide rather than move to prevent it,” the authors write. In practice, if the U.S. tried to pull off a secret sprint to superintelligence, China (or another major power) would almost certainly detect signs and respond – possibly with cyber sabotage, sanctions, or even military posturing to stop it. The trio’s advice is that the U.S. should focus on deterrence and safety more than a unilateral quest for superhuman AI. Notably, Eric Schmidt – who once championed greater urgency to beat China – appears to agree now that a covert crash program might do more harm than good by destabilizing the strategic balance. Thus, the expert consensus leans toward transparency and cooperation at least to the extent of avoiding misunderstandings that could trigger conflict. If a secret program exists, its managers likely understand this and might be proceeding cautiously, or ensuring some lines of communication (back-channel assurances to allies/rivals that “we won’t deploy anything uncontrollable”, for example).

In conclusion, the reality of U.S. AI efforts today is a blend of the overt and covert. The public-facing programs (DARPA, DOE, etc.) and corporate labs already constitute an enormous Manhattan Project in aggregate – one conducted in the open, with global participation. Any truly secret AI project would augment this, not replace it. It might aim to guarantee that the U.S. is first to achieve safe superintelligence (so that it can set the rules for use), or to develop countermeasures against AI threats without alerting adversaries. If it’s happening, we may only learn of it decades from now (as with ULTRA or stealth fighters), or perhaps never if it stays in the shadows. But even without a singular secret project, the United States’ overall AI enterprise has the scale and intensity of a Manhattan Project, distributed across many initiatives.

Conclusions: Is the U.S. Already Running an AI Manhattan Project?

Looking across the landscape, the United States is indeed mounting a mega-project for AI supremacy, though not in the classic form of the 1940s Manhattan Project. Instead of one centralized effort, it’s a sprawling ecosystem of programs – spanning DARPA labs, military commands, national supercomputers, and AI firms from Silicon Valley to Seattle – all collectively pushing toward the cognitive high ground. By any measure, the resources devoted are enormous: federal funding for AI R&D has surged (with hundreds of millions annually in DoD alone), and private investment is even greater. The strategic intent is clear as well. Leaders from both political parties and industry have coalesced around the view that winning the “AI race” is a national priority. Whether explicitly labeled or not, the U.S. has something akin to an AI Manhattan Project in progress: a concerted national effort to develop the most powerful AI systems on Earth, and to do so before rival nations can match or surpass them.

However, there are key differences that make this “AI Manhattan Project” a new kind of endeavor. First, it is far more public and corporate-driven than the original Manhattan Project. Much of the innovation happens in open forums or commercial products (e.g. ChatGPT’s public deployment) rather than behind barbed wire. This openness has benefits – it spurs rapid progress and broadens the talent base – but also means the U.S. cannot fully control the diffusion of AI technology. In essence, America’s AI dominance strategy relies on maintaining its innovative edge in a globalized field, rather than locking down breakthroughs in secrecy (An AI Manhattan Project is Not Inevitable — EA Forum) (An AI Manhattan Project is Not Inevitable — EA Forum). In a sense, the de facto Manhattan Project is “open-source” to a degree; the U.S. leverages its vibrant private sector and academic labs as the engine of progress, then plans to exploit that progress for national advantage.

Second, the U.S. effort is increasingly tempered by safety and deterrence considerations. Unlike the headlong rush of the atomic bomb program, today there is wide recognition of the catastrophic risks uncontrolled AI could pose. This has led to a dual mandate: win the race, but don’t wreck the world in the process. The concept of Mutual Assured AI Malfunction (MAIM) captures why a pure sprint is perilous – any attempt at unchecked dominance might trigger sabotage or a crisis. Thus, U.S. strategists are trying to craft an approach that combines competitive drive with cooperative guardrails. We see nascent efforts at international dialogue on AI (for example, U.S.-China talks about AI safety, or the U.S.-EU discussions on AI standards) intended to reduce the chance of miscalculation. Domestically, the U.S. government has also introduced some regulation and oversight – e.g. the 2023 Executive Order on AI requiring red-team testing of advanced models for safety, and the establishment of an AI Safety Institute. These are arguably parts of a “Manhattan Project for AI Safety” running in parallel to the race for capability.

So, is the U.S. running a de facto AI Manhattan Project? In practical terms, yes – the magnitude of effort and the rhetoric of national mobilization suggest that the spirit of Manhattan Project 1 is alive in America’s AI quest. When a sitting Energy Secretary proclaims we are at the “start of Manhattan Project 2” (Dan Hendrycks warns America against launching a Manhattan Project for AI), and commissions recommend Manhattan-style crash programs (US Commission Calls for 'Manhattan Project' to Beat China in AI Race - Business Insider), it’s not an exaggeration but a reflection of ongoing policy. The U.S. has marshaled its top minds (from academia, industry, and government), is spending lavishly on AI research and infrastructure, and is fixated on beating adversaries to the next big breakthrough. At the same time, no single entity directs this effort with the unity of command that General Groves had in 1945 – instead, it’s a coordinated push across many fronts. This more diffuse approach has strengths (resilience, diversity of ideas) and weaknesses (potential duplication, slower consensus). It also means the U.S. “AI Project” is somewhat self-organizing, with government often in a supporting and orchestrating role rather than building the key systems itself (An AI Manhattan Project is Not Inevitable — EA Forum).

The implications for national strategy are profound. If the U.S. succeeds in maintaining leadership in AI, it will bolster not only its military power but its economic dynamism and soft power (as U.S. companies set global AI norms and standards). American-led AI could accelerate scientific discovery, boost productivity, and help address global challenges – essentially securing the “cognitive high ground” for the free world. But if mismanaged, the AI race could undermine global stability. An arms race mentality without communication could lead to AI deployment outpacing safety, or trigger conflicts due to fear of surprise advantage. Moreover, even winning the race could be pyrrhic if the technology isn’t safe: a runaway superintelligence built in America is just as dangerous as one built elsewhere.

Thus, U.S. strategy going forward must continue to balance assertiveness with caution. Concrete steps might include forging international agreements on certain AI limits (analogous to arms control), investing heavily in AI safety research (so that advanced AI can be controlled and aligned with human values), and developing strong defenses against AI-enabled threats (cyber or misinformation attacks, automated warfare, etc.). On the home front, it will involve nurturing the AI talent pipeline and semiconductor manufacturing (to retain the hardware edge) – the Biden administration’s CHIPS Act and science funding boost are moves in that direction. On the global stage, it means working with allies to present a united front on responsible AI development, so that liberal democracies collectively maintain an advantage over authoritarian regimes in AI capabilities without sparking open conflict.

In summary, the United States is all-in on the race for cognitive dominance, with an array of Manhattan Project-like initiatives driving progress. We are witnessing an unprecedented mobilization of intellect and capital aimed at shaping the future of AI. By many accounts, this is America’s next moonshot – except this time, reaching the “moon” first might be necessary just to ensure the journey doesn’t end in disaster for everyone. The coming years will test whether the U.S. can navigate this tricky course: achieving AI supremacy and steering the technology toward safe and democratic use. The outcome will define the security and prosperity of the 21st century. In the words of one expert, “Given the stakes, superintelligence is inescapably a matter of national security” – and the U.S. is treating it as such, Manhattan Project and all.