ChinAI #212: Engines of Power: Electricity, AI, and General-purpose Military Transformations

Plus, finishing last week's Endless Job Hunt translation

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If you’re interested in what I do as my main hustle, please read and share my new article (open access) with Allan Dafoe, published in European Journal of International Security: “Engines of Power: Electricity, AI, and General-purpose Military Transformations.”

If you want a summary of the main argument, I wrote a short thread last week:

Jeffrey Ding @jjding99

Delighted to share my new article w/ @AllanDafoe, published open access in @EJIntSec *Engines of power: Electricity, AI, and general-purpose military transformations* @GWtweets @ElliottSchoolGW @GovAI_ @StanfordCISAC Key takeaways below:

cambridge.orgEngines of power: Electricity, AI, and general-purpose, military transformations | European Journal of International Security | Cambridge C…Engines of power: Electricity, AI, and general-purpose, military transformations

By studying the history of military electrification, we derive insights about how general-purpose technologies (like electricity and AI) could shape the military balance of power. Most relevant to ChinAI readers, our conclusions challenge the conventional wisdom about how AI could affect U.S.-China military competition:

First, speculation about how AI will transform military affairs places excessive emphasis on the narrow effects of weapon systems. Possibly influenced by popular images of killer robots, both policymakers and scholars focus on autonomous weapons as the primary military application of AI. US defense intellectuals highlight how China could take advantage of AI-enabled hypersonic missile systems to leapfrog US military power.¹ In its approach to AI, the Chinese military also tends to prioritise ‘trump card’ or ‘assassin's mace’ weapons that can counter US capabilities.²

In contrast, a GMT (general-purpose military transformation) approach emphasises the accumulation of AI-enabled improvements across many military systems. This impact pathway will likely interact with weapons capabilities, as was the case with electricity and fire control. Overall, though, effects of AI advances will be more consequential in other military domains, including communications, decision support, intelligence, and logistics.³ Moreover, the focus on AI weapons neglects the indirect effects of AI's potential to upgrade a nation's productive capabilities. AI applications that improve the efficiency and adaptability of manufacturing lines could have significant follow-on effects for military readiness.

Second, existing conjectures about the impact of AI on military affairs severely underestimate the timeframe for when substantial effects will occur. Recent influential articles on AI and national security converge on the next ten to twenty years as the timeframe for when AI will substantially transform military power.⁴ This is reflective of a broader tendency to conflate rapid progress in a technological field, which is characteristic of GPTs, with rapid adoption across military applications, which is uncharacteristic of GPTs.

GMT theory suggests a different view. Economists have already begun to model implementation lags in the effects of AI on economic productivity.⁵ A similar extended trajectory will apply in the military realm. The current wave of AI development started with breakthroughs in deep learning in the early 2010s, so if AI follows the same timeline as electricity, a prolonged period of gestation could extend until around the 2050s.⁶ In addition, since the development of AI is still in its early stages, the foreseeability of its military applications is very limited. Twenty years after the introduction of the electricity dynamo, even the most astute observers of military transformation could not envision how that technology would transform military affairs. As only a decade has passed since critical breakthroughs in deep learning, any attempt to foreordain the ultimate military implications of AI should be met with deep scepticism. Our imaginations – to borrow language from the ordnance engineer quoted earlier – are not sufficiently elastic.

Lastly, GMT theory supplements existing thinking about international diffusion of military applications of AI and the effect of AI on the military balance of power. Some scholars argue that if military advances in AI continue to be closely linked to civilian applications, then military AI capabilities will rapidly diffuse to other countries.⁷ Informed by a historical perspective of GMTs, we view ‘military AI technology’ as not a singular technological innovation but part of a GPT trajectory, which encompasses a broad distribution of technological applications. Just like the organisational requirements for adopting wireless telegraphy were different from those required to adopt searchlights, the adoption capacity for different military applications of AI will vary.

Many scholars and analysts posit that AI and GPTs could narrow gaps in military capabilities between the U.S. and China because military applications could be closely tied to the civilian technology advances (which diffuse quickly around the world). We show that the opposite could be true, especially if a leading military (say, the U.S.) has stronger connections with its civilian base in AI than other militaries (e.g., China and Russia). *Note: If you’ve been steeped in concerns about China’s civil-military fusion fusion drive, you may balk at these claims. But recall that China didn’t even allow non-government capital into national defense industries until 2005, and it is still very much trying to catch up to the U.S. and other developed countries in terms of building a defense industrial base. For example, there is no Chinese equivalent of Palantir (a big data analytics firm founded by the CIA’s venture arm).

To more fully account for how AI advances will differentially advantage certain militaries, more attention should go to factors that apply across the broad front of a GPT trajectory. We highlight the significance of a state's industrial capacity to provide AI infrastructure and skilled labour to militaries. Specifically, militaries able to draw from a wide skill base in AI will better exploit the AI-based GMT. Crucially, the talent base required for AI differs from the talent base required for other dual-use technologies like nuclear power. GMT theory suggests that military linkages to a wide base of AI engineering talent, rather than star researchers or cutting-edge technical capabilities, are crucial to adapting generalised models to a variety of specific military applications.⁸ If leading militaries have stronger connections to their civilian GPT sector than challengers, then GMTs could reinforce existing military balances.

For example, Russia's civilian sector ‘is so far behind other countries in its efforts to develop AI that its start-ups and researchers barely register.’⁹ In the information technology domain, linkages between China's military and civilian sectors are weak.¹⁰

*I’ll be back with a longer Four to Forward next week (I need to catch up on some reading).

Feature Translation: The Endless Job Hunt (part 2)

Context: Last week, I featured this first half of a longform article (link to original Chinese) about the fall recruitment season for college students in China, published by Renwu (人物) magazine. The second half of the article unpacks how graduates felt like they were “being farmed as fish.”

Key Passages: On long-winding recruitment processes that result in no one being hired:

“Various speculations began to spread among the fresh graduates. Some people say that these companies use school recruitment as a brand promotion position: ‘They only release one or two HCs (headcount), and whether they recruit people or not, they have to undertake a massive autumn recruitment project.’ Some people also say that the more people invited to interview, the more KPI can be added to HR. Many interviews are nothing more than a formality for HR to raise its own KPI. Cui Chao (who works in campus recruitment for a large tech company) believes that this is ‘nonsense’, but on social media, complaints about the interview company's ‘fish farming’ are the most common issue of dissatisfaction among job seekers.”

Yu Xin, the Wuhan University master’s student we read about last week, reflects on her experience:

“‘At the beginning, I thought it was so new. AI interviews were new, written tests were new, group interviews were new, and interviews with leaders were new…’ But the further you go, the more exhausted you feel. ‘It's so tortured and uncomfortable. You will feel that you are not special at all. Everyone has what you have, and you will attack yourself a little bit.’

She started to get acne, and her menstrual period became abnormal. She lay in bed at night with ‘a lot of things added’ on her mind. ‘I'm always thinking, when is my company's interview coming? I'm afraid I'll miss it’…

…She said, "You go to listen to the pain of others, and then you will feel that everyone is like this, especially those students who are also very good, even PhD students, they are also suffering, and their sunk costs are higher.” She saw some PhD students, who did not plan for the job search in advance after coming back from studying abroad for many years. They were shocked by this year’s fall recruitment season – so far, they still have zero offers. There are also some PhD students who found out after finishing their studies that they "can't get into positions that they could have gotten into after a master's degree."

The difficulties of the group confused Yu Xin a lot. On the other end of the phone, she asked me, ‘Have you done any research and figured out why this is happening this year?’ Then she also asked, ‘For people like you who have been working for a long time, will you feel a sense of this crisis?’ My answer did not comfort her, and she asked again, ‘Then next year, and the year after, will it be okay?’

More in FULL TRANSLATION: The Endless Job Hunt

Thank you for reading and engaging.

These are Jeff Ding's (sometimes) weekly translations of Chinese-language musings on AI and related topics. Jeff is an Assistant Professor of Political Science at George Washington University.

Check out the archive of all past issues here & please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay for a subscription will support access for all).

Any suggestions or feedback? Let me know at chinainewsletter@gmail.com or on Twitter at @jjding99

1. Robert O. Work and Greg Grant, ‘Beating the Americans at their Own Game’, Center for a New American Security (6 June 2019).

2. Kania, ‘Battlefield Singularity’, pp. 33–4.

3. Horowitz, ‘Artificial intelligence, international competition, and the balance of power’; Kania, ‘Battlefield Singularity’.

4. Greg Allen and Taniel Chan, ‘Artificial Intelligence and National Security’, Belfer Center for Science and International Affairs, Cambridge, MA (2017), p. 61; Horowitz, ‘Artificial Intelligence, international competition, and the balance of power’, p. 42; Payne, ‘Artificial Intelligence’, p. 10.

5. Brynjolfsson et al., ‘Artificial Intelligence and the Modern Productivity Paradox’.

6. Some evidence indicates the waiting time for a significant productivity boost from a new GPT has decreased over time. Crafts, ‘The Solow Productivity Paradox in Historical Perspective’.

7. Drezner, ‘Technological change and International Relations’; Horowitz, ‘Artificial Intelligence, international competition, and the balance of power’.

8. James Ryseff, ‘How To (Actually) Recruit Talent for the AI Challenge’, War on the Rocks (blog) (5 February 2020).

9. Andrew S. Weiss, ‘New Tools, Old Tricks: Emerging Technologies and Russia's Global Tool Kit’, Carnegie Endowment for International Peace (blog) (29 April 2021)

10. Cheung, Tai Ming, Fortifying China: The Struggle to Build a Modern Defense Economy (Ithaca, NY: Cornell University Press, 2013)