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With the increased focus on critical technologies such as AI and quantum computing, further breakthroughs in ASW technologies are likely. For example, the current development of AI and sensor technology for ocean monitoring, as well as advancements in underwater communications, will likely assist in making the ocean “transparent” by 2050. This is highly concerning, as gaining the ability to track and surveil strategic submarines will significantly reduce confidence in nuclear-armed states’ second-strike capability.

The strategic implications of artificial intelligence run deep as they have the potential to upend the fundamental calculus behind deterrence by putting the premium on offence and pre-emption (Rickli 2017). Lynn-Jones notes that “conflict and war will be more likely when offence has the advantage, while peace and cooperation are more probable when defence has the advantage” (Lynn-Jones 1995, 691). Therefore, as argued by Rickli, the impact that AWS have on the offence-defence balance is an essential element in assessing its implications for strategic stability (Rickli, 2018). The use of AI in command-and-control structures, such as automated decision-making and weapons in the form of AWS, risks tilting the balance towards the offence.

First, at the tactical level, how AI-powered weapon systems are likely to be used largely favours offensive use (Rickli 2018). One such example is swarming tactics, which aim at overwhelming enemy defence systems and neutralising any advantage gained by a defensive posture. Following Lynn-Jones’s argument, an environment in which defensive postures are likely to be inefficient enhances the possibility of conflict and strategic instability by incentivising pre-emptive strikes. Second, the opacity inherent to artificial intelligence algorithms similarly affects the offence-defence balance, as it leads to an incapacity to appropriately judge an adversary’s capabilities and their impact (Horowitz 2018, 55). Unlike traditional military capabilities, which can be more easily observed, a state’s armament in AI-related capabilities would be almost impossible for another state to measure accurately. Assessing the depth of automation of an adversary’s arsenal, the quality of the code, the efficiency of the autonomous weapons, and their capabilities would prove difficult. This could create an environment where states are pushed to assume the worst of their rivals and over-estimate their capabilities (Horowitz 2018, 55).

Artificial intelligence is already used today to automate and assist in the production and spread of online disinformation, eroding trust in democratic institutions and processes and sowing confusion and polarisation among populations. Disinformation is not new, but in a digital era where most of the global population is connected, disinformation is gaining traction as an effective tool of asymmetric warfare to influence and destabilise states, while remaining below the threshold of war (Kaspersen and Rickli 2016). Russia notoriously influenced the 2016 US presidential election by purchasing ads on Facebook and Google, utilising automated bot accounts on Twitter, and using troll farms to create and spread disinformation online ahead of the election and disrupt the American democratic process (Polyakova 2018; Mueller 2019). The deliberate targeting of democratic discourse is a new type of political warfare which can be “employed to disrupt free societies anywhere in the world” (Arquilla 2021, 2).

Advances in Natural Language Processing (NLP), the branch of artificial intelligence that focuses on training computers to understand, process, and replicate human language, can have very concerning applications. Open AI recently developed the ChatGPT, which can automatically generate text about any subject by learning from massive amounts of English text articles online.3 The texts can be persuasive and nearly impossible to distinguish from text written by humans. Such capabilities have the potential to scale up a generation of misleading news articles, the impersonation of others online, the automation of the production of abusive or fake content posted on social media, or the automated production of spam and phishing content (Open AI 2019). AI enables increased dissemination of propaganda and provides “mechanisms to narrowly tailor propaganda to a target audience” (Horowitz et al. 2018, 4). With advances in machine learning and the growing amount of individual data collected on the internet, AI can profile individuals with extreme precision, predicting their preferences and behaviours (Mansted and Rosenbach 2018). This enables the kind of impact seen in the 2016 elections. It is now possible to identify those most susceptible to being influenced, targeting their fears, characteristics, vulnerabilities, and beliefs (Mansted and Rosenbach 2018). The seriousness of the threat has prompted the USAF to develop a programme with Primer (a startup company specialising in NLP) to develop a software tool to detect false narratives in public space (Tucker 2020).

The incorporation of AI into military systems could also pose new challenges as far as nuclear risk reduction is concerned, as it could increase the risk of escalation into a nuclear conflict either accidentally (i.e. as a result of technical failure or unauthorized use) or inadvertently (i.e. as ‘the unintended consequence of a decision to fight a conventional war’).17 In politically sensitive situations, such as an intentional or unintentional collision at sea, increasing reliance on AI systems could turn out to be highly problematic. Such a dependence could lead humans to take premature or misguided decisions and actions that could trigger an armed conflict, and possibly even a nuclear war.

Artificial intelligence could destabilize the delicate balance of nuclear deterrence, inching the world closer to catastrophe, according to a working group of experts convened by RAND. New smarter, faster intelligence analysis from AI agents, combined with more sensor and open-source data, could convince countries that their nuclear capability is increasingly vulnerable. That may cause them to take more drastic steps to keep up with the U.S. Another worrying scenario: commanders could make decisions to launch strikes based on advice from AI assistants that have been fed wrong information.

Last May and June, RAND convened a series of workshops, bringing together experts from nuclear security, artificial intelligence, government, and industry. The workshops produced a report, released on Tuesday, that underlines how AI promises to rapidly improve Country A’s ability to target Country B’s nuclear weapons. And that may lead Country B to radically rethink the risks and rewards of acquiring more nuclear weapons or even launching a first strike.“Even if AI only modestly improves the ability to integrate data about the disposition of enemy missiles, it might substantially undermine a state’s sense of security and undermine crisis stability,” the report said.

Further, the application of AI technology has a marked impact on strategic stability among nuclear powers and may undermine the mutual vulnerability and strategic stability of nuclear-armed states of unequal power. Because AI boasts strong capabilities in image and pattern recognition, similar to facial recognition, its ability to recognize still images is strong and it will greatly enhance the effectiveness of strategic reconnaissance. By improving the ability to interpret satellite images and the deep reconnaissance of long-distance UAVs, the country that possesses nuclear superiority will be able to gain further intelligence on the basic characteristics and procedures of its opponent’s nuclear force deployment and movements. This will enhance a country’s confidence in its ability to disarm its opponent’s nuclear weapons with a first strike. When there is a large gap between the nuclear forces of the two countries, this situation will make a preemptive strike by the more powerful one more advantageous, thus weakening the strategic stability of the two countries during a crisis.

At sea, AI technology can be used to improve the capability to collect and process a submarine’s sound signature. In anti-submarine warfare, UUVs can also be used to conduct close-range reconnaissance, which can strengthen the capacity to detect and recognize enemy nuclear-powered ballistic missile submarines (SSBNs). On the one hand, this reduces the survivability of the enemy SSBNs, thereby weakening the effectiveness of nuclear deterrence and reducing strategic stability.1 On the other hand, this AI technology can reduce the probability of accidental nuclear war: by improving the ability to identify SSBNs, a country can avoid accidentally hitting an SSBN as part of conventional anti-submarine warfare.

Potential U.S. adversaries, such as Russia, take seriously the possibility that the United States might leverage its advantage in such technologies as AI to radically improve its counterforce capabilities. For the past several years, Russian military analysts have been engaged in a vociferous debate in the military press about the extent of their country’s strategic vulnerabilities.8 Their tendency to assume that current and future U.S. capabilities pose a dire threat to Russia’s security stokes these anxieties.

A major challenge of nuclear strategy is that adversaries may interpret one nation’s secure retaliatory forces as a first-strike threat or a doomsday machine and react accordingly. For instance, the Russians probably conceived of Status-6 as a last-ditch second-strike option exploiting AI to autonomously circumvent U.S. defenses, but Western observers interpreted it as a Strangelovean “cobalt bomb.” AI progress is also contributing to Russia’s doubling down on older types of systems with undesirable strategic properties. For example, with its RS-28 “Sarmat” missile, Russia is reinvesting in large, silobased ICBMs with multiple independently targetable reentry vehicle (MIRV) warheads, a category of weapon it once planned to abandon under the now-defunct Strategic Arms Reduction Talks II treaty. Western strategic theory generally considers large MIRVed ICBMs to be destabilizing because they are ideal for preemptive strikes and are vulnerable to preemption.

Some branches of the U.S. military are hitting the brakes on generative AI after decades of Department of Defense experiments with broader AI technology.¶ Why it matters: As businesses race to put generative AI in front of customers everywhere, military experts say its strengths and limitations need further testing and evaluation in order to deploy it responsibly.¶ Driving the news: In a new essay in Foreign Affairs, Jacquelyn Schneider, the Hoover Institution's director of the wargaming and crisis simulation initiative, and AI researcher Max Lamparth write that large language models have a potentially dangerous tendency to go nuclear.¶ When they tested LLMs from OpenAI, Anthropic and Meta in situations like simulated war games, the pair found the AIs suggested escalation, arms races, conflict — and even use of nuclear weapons — over alternatives.¶ "It is practically impossible for an LLM to be taught solely on vetted high-quality data," Schneider and Lamparth write.

A core important reason why war has not erupted among major powers from the cold war until today has been the existence of nuclear weapons. However, AI and its military applications are likely to engender conflict as it undermines nuclear strategic stability. This is particularly the case when AI technology is combined with remote sensing and unmanned aerial vehicle (UAV) surveillance, which assist in more accurate detection of an opponent’s deployment of strategic power. This reduces strategic stability, which is predicated on a certain level of uncertainty. Additionally, some autonomous weapon platforms, including unmanned underwater vehicles (UUVs), increase the risks of accidental launch and nuclear war. They do this through the potential for accidents and lack of human control.

The two extreme cases have only limited relevance for the future of nuclear warfare. Stalling development (also referred to as AI winter) would result in only minor changes from the current nuclear-security environment. With superintelligence, AI would render the world unrecognizable and either save or destroy humanity in the process. The other two cases, in which AI progresses substantially and enables many new capabilities while still remaining fallible and inferior to humans in at least some respects, seem to have more support from the expert community, although experts disagree about the national security implications of such capabilities. Some fall in the category of “Complacents”: These tend to believe that producing an AI capable of performing the types of tasks that would destabilize the nuclear balance is sufficiently difficult that it is unlikely to be achieved. “Alarmists” hold the opposite view, that an AI could be capable of certain tasks but should not be included in any aspect of nuclear war. A third group, “Subversionists,” focus on an adversary’s ability to alter, mislead, divert, or otherwise trick the AI, which could prove either stabilizing or destabilizing.

Other examples of artificial intelligence, there’s facial recognition technology being used to identify civilians and casualties, for example. They’re being used to—they’re using natural language processing, which is a type of artificial intelligence that kind of analyzes the way people speak.

You think of Siri. You think of chat bots. But more advanced versions being used to kind of read in radio transmissions and translate them and tag them so that they’re able to—that forces are able to go through more quickly and identify what combatants are saying.

Furthermore, involvement of AI technology will also introduce new variables into the stability of the global system of nuclear deterrence. In an era of weak AI, only a combination of AI technology and nuclear weapons can form an effective deterrent system. When AI technology is involved in all aspects of a nuclear deterrent structure, the original system of stability will change. As a data tool, AI provides countries with new offensive capabilities and has a direct impact on the reliability of nuclear weapon use. In a big data environment, however, there are also a number of subjective factors, such as the unpredictability of national will and strategic intent. When subjected to deep learning algorithms, intent may be clarified. These shifts could result in an imbalance in the MAD-based system of nuclear deterrence. The party with a command of AI technology will have the ability to clearly assess the possibility and destructiveness of the other party’s nuclear counterattack, thus having more flexible strategic options, while the side with relatively backward technologies will possess less credible retaliatory capabilities.41 The gap between military powers will again expand and countries’ military strategic aims will accordingly readjust. In other words, the traditional international security system will become unstable.

But being wrong is the least of it. The idea that technology is “inevitable” is downright dangerous.

The mushroom cloud one hour after an atom bomb was dropped on Hiroshima.

It is widely expected that artificial intelligence (AI) will disrupt and fundamentally transform many aspects of social, economic, and political life over the coming decades.2 As we are in the early stages of AI adoption in most fields, many discussions of its potential necessarily impact remain vague and superficial, including where national security is concerned. 3 Nevertheless, some scholars have productively explored AI’s impact on the character of warfare,4 the balance of power among states,5 and human society itself. 6 This article contributes to this growing body of literature by examining how this new technology might interact with one of the most prominent features of the post-World War II strategic environment: nuclear weapons. Specifically, it asks whether AI driven improvements to intelligence, surveillance, and reconnaissance (ISR) capabilities, i.e., automated image recognition/computer vision, could enable an effective counterforce capability and thereby imperil nuclear deterrence and weaken first-strike stability. 7 Since the advent of the nuclear age, analysts have been divided over the meaning of nuclear weapons for international politics. Proponents of the “nuclear revolution” argue that nuclear weapons, particularly a secure second-strike capability, which would allow a state to retaliate with nuclear force after suffering a first strike, satisfy a state’s security needs because they present a potential for unacceptable damage that would deter any potential challenger from even considering aggression.8 In their view, international politics in the shadow of nuclear weapons would be largely peaceful, competition would be relegated to the margins, and nuclear superiority would be meaningless.9 However, recent research suggests that this may not be the case. In fact, nuclear weapon states have sought to hold at risk the nuclear arsenals of their adversaries throughout the nuclear age.10 Still, no country has attacked another’s operational nuclear arsenal.11 That is because the requirements for a disarming strike are considerably higher than for damage limitation or retaliation against nuclear attack. A state trying to disarm its opponent would have to be certain to destroy, or at least disable, all of its adversary’s nuclear weapons. Even one operational nuclear warhead could inflict unacceptable damage in a retaliatory blow. The inability to be certain that a first strike could find and eliminate all enemy nuclear weapons has been the main roadblock for effective counterforce.12 As the number of battlefield sensors grows and ISR capabilities improve, however, this roadblock is crumbling. Some expect it to be fully overcome with the help of AI.

2023 was the year when the hype around artificial intelligence (AI) went into hyperdrive. Following its release in late 2022, ChatGPT-3 made AI technology accessible and genuinely useful to the general public, prompting the development of numerous other Large Language Models (LLM) by some of Silicon Valley’s mightiest giants. AI was the word on everyone’s lips last year, but could it be set to enter a period of stagnation?¶ Rodney Brooks believes so. Brooks is a former director of the Computer Science and Artificial Intelligence Laboratory at MIT who regularly comments on technology's progress (or lack thereof). Since 2018, he has posted his predictions of self-driving cars, human space travel, and – last but not least – robotics, AI, and machine learning. He’s promised to keep making the forecasts each year until 2050 when he’ll turn 95.¶ In his latest scorecard, Brooks predicted that 2024 won’t be a golden age for AI, noting that the current fanfare is “following a well worn hype cycle that we have seen again, and again, during the 60+ year history of AI.”¶ “Get your thick coats now. There may be yet another AI winter, and perhaps even a full-scale tech winter, just around the corner. And it is going to be cold,” Brooks concluded.¶ Brooks is far from a pessimistic Luddite. He’s been studying AI since the 1970s and has been dubbed “one of the world’s most accomplished experts in robotics and artificial intelligence.” If he seems cynical, it’s simply because he’s seen it all before; all the publicity, letdowns, false promises, and setbacks. Take a look at his former predictions and you’ll see his technological prophecies are often right on the money.

In an extreme case, AI could undermine the condition of MAD and make nuclear war winnable, but it takes much less to undermine strategic stability. AI advancements merely need to cast doubt on the credibility of retaliation at some level of conflict. Major nuclear powers, such as the United States, Russia, and China, have a shared interest in maintaining the credibility of central deterrence, but they seek regional advantages in pursuit of what they regard as their core strategic interests. Areas where credibility is already strained, such as certain extended deterrence guarantees, are particularly vulnerable to destabilization. The increasingly multipolar strategic environment is also encouraging forms of competition that threaten stability. For instance, the United States is interested in developing the capability to track and target a minor nuclear power’s mobile missile launchers, but Russia and China fear that the same technology could mature into a threat to their more sophisticated retaliatory forces. In a crisis situation, the employment or availability of AI-enabled intelligence, surveillance, and reconnaissance (ISR) or weapon systems could stoke tensions and increase the chances of inadvertent escalation. Finally, the pursuit of advanced military capabilities is liable to cause arms race instability even if those technologies are nonviable, as in the historical case of missile defense.

The challenge AI poses to strategic stability is not unique to this particular technology, but it is more acute because of rapid technical progress in AI and its many potential intersections with nuclear strategy. Most of the specific applications AI are likely to be used for, such as analysis of ISR data, controlling autonomous sensor platforms, and automated target recognition (ATR) have been eagerly sought for decades but were beyond the capability of available technology. Even without further breakthroughs, incremental progress using existing AI techniques may make these long-sought goals practical realities in the foreseeable future.

Similarly, AI is sparking military systems evolution in the United States and China, two countries that lead in global AI development by a number of different markers, but not yet in a militarily transformative way. Military systems evolution refers to the ways that new technologies are incorporated into new or existing military capabilities and applications. AI applications have advanced in autonomy and robotics, decision support, predictive maintenance and logistics, cyber, and modeling and simulation, yet are subject to many of the weaknesses mentioned above; progress is generally broader than it is deep. Many higher-profile AI-enabled programs in the unclassified realm across different types of applications, such as Joint All-Domain Command and Control (JADC2) or Loyal Wingman, are comparatively nascent. Military access to the useful, clean, and comprehensive data sets necessary to train AI applications can be limited.

As such, AI has done little to spark either operational innovation or organizational adaptation in the United States and China. Operational innovation stems from critical thinking about how militaries can capitalize on new technologies and systems to achieve their objectives; organizational adaptation connotes changes to the structure of the fighting force to exploit new systems and patterns of operations. For example, German blitzkrieg warfare centered new operations around speed and mechanized (tank) maneuvers, leading to the organizational adaptation of separating tank units from soldiers moving on foot.

The chemical industry is currently facing several challenges, which will likely still be present in the next few years: China has a strong influence on the global chemical market, while Europe and the US steadily lose their share. Additionally, albeit being necessary, sustainability-driven regulations like the European Green Deal threaten to negatively impact the chemical industry further on their way to reach a net-zero future.

At the same time, many chances are currently present that can be leveraged to overcome or mitigate these problems:

Another hypothesized planetary boundary entails the conversion of natural habitats to agricultural land. The mechanism by which too much agricultural land could cause a crisis is unclear—unless it is because land conversion causes so much biodiversity loss that species extinctions are the proximate cause of an ecocatastrophe. Excessive chemical pollution and excessive atmospheric aerosol loading have each been suggested as planetary boundaries as well. In the case of these pollution boundaries, there are well-documented mechanisms by which surpassing some concentration of a pollutant inflicts severe human health hazards. There is abundant evidence linking chemical and aerosol pollution to higher mortality and lower reproductive success in humans, which in turn could cause a major die-off. It is perhaps appropriate then that when Hollywood envisions an unlivable world, it often invokes a story of humans poisoning themselves. That said, it is doubtful that we will poison ourselves towards extinction. Data show that as nations develop and increase their wealth, they tend to clean up their air and water and reduce environmental pollution (Flörke et al., 2013; Hao & Wang, 2005). In addition, as economies become more circular (see Mathews & Tan, 2016), environmental damage due to waste products is likely to decline. The key point is that the pollutants associated with the planetary boundaries are so widely recognized, and the consequences of local toxic events are so immediate, that it is reasonable to expect national governments to act before we suffer a planetary ecocatastrophe.

Arguably the most damaging byproduct of Khan’s anti-merger overreach is that it is hurting American global competitiveness and innovation, even in fields such as life-saving medicines. For example, when the FTC filed suit against pharmaceutical giant Amgen for its acquisition of Horizon Therapeutics despite the companies having no overlapping products (the first time this has ever happened for any pharmaceutical company) and the FTC was closely scrutinizing Pfizer’s purchase of Seagen to advance no-overlap cancer drugs, even Amgen and Pfizer’s competitors rallied against the FTC.

Pharma industry groups came together to publish a detailed report showing how $18 billion in private venture capital investment and $152 billion in large pharmaceutical company investments into new vaccines and treatments would be practically outlawed overnight, bringing the flow of new drugs to market to a virtual halt and bankrupting thousands of biotech startups overnight. A respected pharmaceutical industry analyst, Jay Olson of Oppenheimer, wrote, “Stop the insanity,” describing the FTC actions as “ludicrous,” echoing the sentiments of many of his peers.