Product safety is a poor model for AI governance

*Rick Korzekwa, February 1, 2023*

*Note: This post is intended to be accessible to readers with relatively little background in AI safety. People with a firm understanding of AI safety may find it too basic, though they may be interested in knowing which kinds of policies I have been encountering or telling me if I’ve gotten something wrong.*

I have recently encountered many proposals for policies and regulations intended to reduce risk from advanced AI. These proposals are highly varied and most of them seem to be well-thought-out, at least on some axes. But many of them fail to confront the technical and strategic realities of safely creating powerful AI, and they often fail in similar ways. In this post, I will describe a common type of proposal[1](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-1-3402 "They seem common to me, based mainly on conversations, posts, and presentations I’ve seen within the AI governance field. It is possible these kinds of proposals are rare, but memorable to me.") and give a basic overview of the reasons why it is inadequate. I will not address any issues related to the feasibility of implementing such a policy.

### Caveats

* It is likely that I have misunderstood some proposals and that they are already addressing my concerns. Moreover, I do not recommend dismissing a proposal because it pattern-matches to product safety on the surface level.
* These approaches may be good when combined with others. It is plausible to me that an effective and comprehensive approach to governing AI will include product-safety-like regulations, especially early in the process when we’re still learning and setting the groundwork for more mature policies.

The product safety model of AI governance
-----------------------------------------

A common AI policy structure, which I will call the ‘product safety model of AI governance’[2](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-2-3402 "I am not at all an expert on real product safety regulations. This is meant mainly as a useful comparison, not a criticism of product safety regulation in general."), seems to be built on the assumption that, while the processes involved in creating powerful AI may need to be regulated, the harm from a failure to ensure safety occurs predominantly when the AI has been deployed into the world. Under this model, the primary feedback loop for ensuring safety is based on the behavior of the model after it has been built. The product is developed, evaluated for safety, and either sent back for more development or allowed to be deployed, depending on the evaluation. For a typical example, here is a diagram from a US Department of Defense report on responsible AI:[3](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-3-3402 "DOD Responsible AI Working Council (U.S.). U.S. Department of Defense Responsible Artificial Intelligence Strategy and Implementation Pathway. 2022.

https://www.ai.mil/docs/RAI_Strategy_and_Implementation_Pathway_6-21-22.pdf

")
[![](https://aiimpacts.org/wp-content/uploads/2023/02/Screenshot-2023-02-01-at-12.40.08-PM-1024x453.png)](https://aiimpacts.org/wp-content/uploads/2023/02/Screenshot-2023-02-01-at-12.40.08-PM.png)
The system in this diagram is not formally evaluated for safety or performance until after “Acquisition/Development”.[4](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-4-3402 "The text of the article does not rule out intervention in development or basic research, but it does not seem to consider this a crucial aspect of ensuring safety.")
I do not find it surprising that this model is so common. Most of the time when we are concerned about risk from technology, we are worried about what happens when the technology has been released into the world. A faulty brake line on a car is not much of a concern to the public until the car is on public roads, and the facebook feed algorithm cannot be a threat to society until it is used to control what large numbers of people see on their screens. I also think it is reasonable to start with regulations that are already well-understood, and work from there. But this model, on its own, is inadequate for AI, for reasons I will explain in the next section.

This model fails when applied to advanced AI
--------------------------------------------

This approach cannot be relied on to prevent the major risks associated with advanced AI. I will give two reasons for this, though there may be others.

### Safety cannot (yet) be evaluated reliably in a fully-developed AI

A fundamental difficulty of ensuring that AI will behave as intended is that examining an AI or observing its behavior during development and testing is not a reliable indicator of how it will behave once deployed. This is a difficult, unsolved problem that resists simple solutions.[5](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-5-3402 "For an accessible and engaging introduction: Christian B. The Alignment Problem: Machine Learning and Human Values. New York, NY: W.W. Norton & Company; 2020. ") I will not explain this problem in detail, but I will list some aspects of it here, along with real-world examples, and you can read more about it elsewhere.[6](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-6-3402 "Amodei D, Olah C, Steinhardt J et al. Concrete Problems in AI Safety. arXiv. Preprint posted online June 21, 2016. https://arxiv.org/pdf/1606.06565.pdf")[7](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-7-3402 "Ngo R. The alignment problem from a deep learning perspective. arXiv. Preprint posted online August 30, 2022. https://arxiv.org/abs/2209.00626")
AI may learn approaches to problems or, if sufficiently advanced, acquire goals that result in behavior that seems to work in training but will fail in deployment.

* A system trained to classify images performed extremely well, but only because it had learned to detect images based on their storage location.[8](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-8-3402 "From Hacker News user api:

“To give a specific example: I once wrote an objective function to train an evolving system to classify images, a simple machine learning test. After running it for only an hour or so, the system’s performance seemed spectacular, like way up in the 90’th percentile. This made me suspicious. The programs that had evolved did not seem complex enough, and past experiments had shown that it should take a lot longer to get something that showed reasonable performance.

After a lot of analysis I figured out what it was.

I was pulling test images from two different databases. One database had higher latency than the other. The bugs had evolved a timing loop to measure how long it took them to get their data (they were multi-threaded) and were basically executing a side-channel attack against the training supervisor.”

https://news.ycombinator.com/item?id=6269114

")
* A simulated robot was trained using human feedback. It was supposed to grasp an object, but instead it learned to place its hand between the object and the camera, giving the appearance of grasping it.[9](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-9-3402 "“in some domains our system can result in agents adopting policies that trick the evaluators. For example, a robot which was supposed to grasp items instead positioned its manipulator in between the camera and the object so that it only appeared to be grasping it, as shown below.”

OpenAI. “Learning from Human Preferences,” June 13, 2017. https://openai.com/blog/deep-reinforcement-learning-from-human-preferences/.

")

AI can find solutions to problems that cannot be reliably evaluated by humans.

* An evolutionary algorithm for designing circuits on a real circuit board created a circuit that produced the desired output, but the researchers were unable to determine how it worked.[10](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-10-3402 "“It has proved difficult to clarify exactly how these circuits work. Probing a typical one with an oscilloscope has shown that it does not use beat frequencies to achieve the target frequency. If the transistors are swapped for nominally identical ones, then the output frequency changes by as much as 30%. A simulation was created that incorporated all the parasitic capacitance expected to exist within the physical circuit, but the simulated circuits failed to oscillate. The programmable switches almost certainly play an important role in the behaviour of the circuit and it is only possible to probe their input and output connections and not the circuitry in which they are embedded.”

Bird, Jon, and Paul Layzell. “The evolved radio and its implications for modelling the evolution of novel sensors.” In Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No. 02TH8600), vol. 2, pp. 1836-1841. IEEE, 2002.

")

AI with goals that are at odds with human interests will have an incentive to avoid behavior that reveals its goals.

* Creatures undergoing simulated evolution learned to identify when they were in an evaluation environment and when they were in the “real” environment, and used this to hide their unwanted behavior.[11](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-11-3402 "“[Charles Ofria, the researcher] tried to disable mutations that improved an organism’s replication rate (i.e. its fitness). He configured the system to pause every time a mutation occurred, and then measured the mutant’s replication rate in an isolated test environment. If the mutant replicated faster than its parent, then the system eliminated the mutant; otherwise, the mutant would remain in the population…However, while replication rates at first remained constant, they later unexpectedly started again rising. After a period of surprise and confusion, Ofria discovered that he was not changing the inputs provided to the organisms in the isolated test environment. The organisms had evolved to recognize those inputs and halt their replication”

Lehman, Joel, Jeff Clune, Dusan Misevic, Christoph Adami, Lee Altenberg, Julie Beaulieu, Peter J. Bentley et al. “The surprising creativity of digital evolution: A collection of anecdotes from the evolutionary computation and artificial life research communities.” arXiv preprint arXiv:1803.03453 (2018).

")

### Unsafe AI can cause substantial harm before deployment

Unlike most technologies, AI can pose a serious risk even during development and testing. As AI capabilities approach and surpass human capabilities, the capacity for AI to cause harm will increase and it will become more difficult to ensure that AI is unable to cause harm outside its development and testing environment. I will not go into lots of detail on this, but I will outline the basic reasons for concern.

#### AI is likely to seek influence outside its intended domain

Whether a given AI system will attempt to gain influence outside the environment in which it is trained and tested is difficult to determine, but we have ample reason to be cautious. Learning to create and execute plans toward a particular goal is something we should expect by default within a broad range of AI applications as capabilities increase[12](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-12-3402 "“Many real-world environments have symmetries which produce power-seeking incentives. In particular, optimal policies tend to seek power when the agent can be shut down or destroyed. Seeking control over the environment will often involve resisting shutdown, and perhaps monopolizing resources.”

Turner AM, Smith L, Shah R et al. Optimal Policies Tend to Seek Power. arXiv. Preprint posted online December 3, 2021. https://arxiv.org/abs/1912.01683

"), and for a system to do well on real-world tasks, it will need to make use of information about the wider world. This is something that current language models are already doing on their own.[13](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-13-3402 "“As one early example, when Degrave [2022] prompted OpenAI’s ChatGPT language model to output the source code at its own URL, it hallucinated code which called a large language model with similar properties as itself. This suggests that the ChatGPT training data contained enough information about OpenAI for ChatGPT to infer some plausible properties of an OpenAI-hosted URL. More generally, large language models trained on internet text can extensively recount information about deep learning, neural networks, and the real-world contexts in which those networks are typically deployed; and can be fine-tuned to recount details about themselves specifically [OpenAI, 2022a]. We should expect future models to learn to consistently use this information when choosing actions, because that would contribute to higher reward on many training tasks.”

https://arxiv.org/abs/2209.00626

") There are also examples of AI finding vulnerabilities that allow it to manipulate things it was not intended to have access to, such as a game-playing AI that exploited a glitch in the game to increase its score directly.[14](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-14-3402 "“In the second interesting solution (https://www. youtube.com/watch?v=meE5aaRJ0Zs), the agent discovers an in-game bug. First, it completes the first level and then starts to jump from platform to platform in what seems to be a random manner. For a reason unknown to us, the game does not advance to the second round but the platforms start to blink and the agent quickly gains a huge amount of points (close to 1 million for our episode time limit)”

Chrabaszcz P, Loshchilov I, Hutter F. Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari. arXiv. Preprint posted online February 24, 2018. https://arxiv.org/abs/1802.08842

")
#### It is difficult to constrain advanced AI to a particular domain

AI with access to the Internet may create copies of itself, gain access to influential systems such as critical infrastructure, or manipulate people into taking actions that are harmful or increase its influence. AI without direct access to the outside world may be able to gain access by manipulating people involved in its development or by exploiting hardware vulnerabilities.[15](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-15-3402 "“Academics from an Israeli university have published new research today detailing a technique to convert a RAM card into an impromptu wireless emitter and transmit sensitive data from inside a non-networked air-gapped computer that has no Wi-Fi card.”

Cimpanu C. Academics Turn Ram into wi-fi cards to steal data from air-gapped systems. ZDNET. https://www.zdnet.com/article/academics-turn-ram-into-wifi-cards-to-steal-data-from-air-gapped-systems. Published December 15, 2020. Accessed October 3, 2022.

")
Executing such an escape from a datacenter may not require very high capability. Surprisingly simple social engineering techniques[16](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-16-3402 "“Phishing emails and text messages often tell a story to trick you into clicking on a link or opening an attachment. You might get an unexpected email or text message that looks like it’s from a company you know or trust, like a bank or a credit card or utility company. Or maybe it’s from an online payment website or app.”

How to recognize and avoid phishing scams. Federal Trade Commission Consumer Advice. https://consumer.ftc.gov/articles/how-recognize-and-avoid-phishing-scams. Published September 2022. Accessed October 3, 2022.

") can be successful at getting a person to do something that is not in their interest. Malware that can propagate between computers and control them has existed for a long time[17](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-17-3402 "

“At around 8:30 p.m. on November 2, 1988, a maliciously clever program was unleashed on the Internet from a computer at the Massachusetts Institute of Technology (MIT).This cyber worm was soon propagating at remarkable speed and grinding computers to a halt. ‘We are currently under attack,’ wrote a concerned student at the University of California, Berkeley in an email later that night. Within 24 hours, an estimated 6,000 of the approximately 60,000 computers that were then connected to the Internet had been hit. Computer worms, unlike viruses, do not need a software host but can exist and propagate on their own.”

The Morris Worm. FBI.gov. https://www.fbi.gov/news/stories/morris-worm-30-years-since-first-major-attack-on-internet-110218. Published November 2, 2018. Accessed October 3, 2022.

") and does not require the software to have any high-level decision making capability at all.

Importantly, detecting such escape attempts may be very difficult. Almost any AI will have a strong incentive not to be observed taking dangerous, unintended actions, and current systems sometimes find unintended solutions that are invisible to humans.[18](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-18-3402 "“In a series of experiments, we demonstrate an intriguing property of the model: CycleGAN learns to ‘hide’ information about a source image into the images it generates in a nearly imperceptible, highfrequency signal.”

Chu C, Zhmoginov A, Sandler M. CycleGAN, a Master of Steganography. arXiv. Preprint posted online December 16, 2017. https://arxiv.org/abs/1712.02950") Additionally, AI in training is subject to the evaluation difficulties explained in the first section.

#### AI breakout could be catastrophic

Once AI has gained influence outside its intended domain, it could cause immense harm. How likely this is to happen and how much harm it may cause is a big topic[19](https://aiimpacts.org/product-safety-is-a-poor-model-for-ai-governance/#easy-footnote-bottom-19-3402 "List of sources arguing for existential risk from AI. AI Impacts. https://aiimpacts.org/list-of-sources-arguing-for-existential-risk-from-ai/. Published August 6, 2022. Accessed October 3, 2022.") that I will not try to cover here, but there are a few things worth pointing out in this context:

* Most of the arguments around catastrophic AI risk are agnostic to whether AI gains influence before or after deployment.
* The basic premise behind catastrophic AI risk is not just that it may use whatever influence it is granted to cause harm, but that it will seek additional influence. This is at odds with the basic idea behind the product safety model, which is that we prevent harm by only granting influence to AI that has been verified as safe.
* The overall stakes are much higher than those associated with things we normally apply the product safety model to, and unlike most risks from technology, the costs are wide reaching and almost entirely external.

Additional comments
-------------------

I am reluctant to offer alternative models, in part because I want to keep the scope of this post narrow, but also because I’m not sure which approaches are viable. But I can at least provide some guiding principles:

* Policies intended to mitigate risk from advanced AI must recognize that verifying the safety of highly capable AI systems is an unsolved problem that may remain unsolved for the foreseeable future.
* These policies must also recognize that public risk from AI begins during development, not at the time of deployment.
* More broadly, it is important to view AI risk as distinct from most other technologies. It is poorly understood, difficult to control in a reliable and verifiable way, and may become very dangerous before it becomes well understood.

Finally, I think it is important to emphasize that, while it is tempting to wait until we see the warning signs that AI is becoming dangerous before enacting such policies, capabilities are advancing rapidly and AI may acquire the ability to seek power and conceal its actions in a sophisticated way with relatively little warning.

Acknowledgements
----------------

Thanks to Harlan Stewart for giving feedback and helping with citations, Irina Gueorguiev for productive conversations on this topic, and Aysja Johnson, Jeffrey Heninger, and Zach Stein-Perlman for feedback on an earlier draft. All mistakes are my own.

Notes
-----


We don’t trade with ants

*(Crossposted from [world spirit sock puppet](https://worldspiritsockpuppet.com/2023/01/10/we-dont-trade-with-ants.html))*

*Katja Grace, 10 January 2023*

When discussing advanced AI, sometimes the following exchanges happens:

“Perhaps advanced AI won’t kill us. Perhaps it will trade with us”

“We don’t trade with ants”

I think it’s interesting to get clear on exactly why we don’t trade with ants, and whether it is relevant to the AI situation.

When a person says “we don’t trade with ants”, I think the implicit explanation is that humans are so big, powerful and smart compared to ants that we don’t need to trade with them because they have nothing of value and if they did we could just take it; anything they can do we can do better, and we can just walk all over them. Why negotiate when you can steal?

I think this is broadly wrong, and that it is also an interesting case of the classic cognitive error of imagining that trade is about swapping fixed-value objects, rather than creating new value from a confluence of one’s needs and the other’s affordances. It’s only in the imaginary zero-sum world that you can generally replace trade with stealing the other party’s stuff, if the other party is weak enough.

Ants, with their skills, could do a lot that we would plausibly find worth paying for. Some ideas:

1. Cleaning things that are hard for humans to reach (crevices, buildup in pipes, outsides of tall buildings)
2. Chasing away other insects, including in agriculture
3. Surveillance and spying
4. Building, sculpting, moving, and mending things in hard to reach places and at small scales (e.g. dig tunnels, deliver adhesives to cracks)
5. Getting out of our houses before we are driven to expend effort killing them, and similarly for all the other places ants conflict with humans (stinging, eating crops, ..)
6. (For an extended list, see ‘Appendix: potentially valuable things things ants can do’)

We can’t take almost any of this by force, we can at best kill them and take their dirt and the minuscule mouthfuls of our foods they were eating.

Could we pay them for all this?

A single ant eats about 2mg per day according to [a random website](https://www.terminix.com/blog/bug-facts/how-much-do-bugs-eat/), so you could support a colony of a million ants with 2kg of food per day. Supposing they accepted pay in sugar, or something similarly expensive, 2kg costs around [$3](https://www.webstaurantstore.com/domino-extra-fine-granulated-sugar-50-lb/104SUGEFG50.html). Perhaps you would need to pay them more than subsistence to attract them away from foraging freely, since apparently food-gathering ants usually collect more than they eat, to support others in their colony. So let’s guess $5.

My guess is that a million ants could do well over $5 of the above labors in a day. For instance, a colony of meat ants [takes ‘weeks’](https://en.wikipedia.org/wiki/Meat_ant#Relationship_with_humans) to remove the meat from an entire carcass of an animal. Supposing somewhat conservatively that this is three weeks, and the animal is a [1.5kg](https://www.environment.nsw.gov.au/topics/animals-and-plants/native-animals/native-animal-facts/bandicoots#:~:text=Bandicoots%20are%20about%20the%20size,of%20which%20live%20in%20NSW.&text=The%20long%2Dnosed%20bandicoot%20is,weighs%20up%20to%201.5kg.) bandicoot, the colony is moving 70g/day. Guesstimating the mass of crumbs falling on the floor of a small cafeteria in a day, I imagine that it’s less than that produced by tearing up a single bread roll and spreading it around, which the internet says is about 50g. So my guess is that an ant colony could clean the floor of a small cafeteria for around $5/day, which I imagine is cheaper than human sweeping (this site says ‘light cleaning’ costs around $35/h on average in the US). And this is one of the tasks where the ants have least advantages over humans. Cleaning the outside of skyscrapers or the inside of pipes is presumably much harder for humans than cleaning a cafeteria floor, and I expect is fairly similar for ants.

So at a basic level, it seems like there should be potential for trade with ants – they can do a lot of things that we want done, and could live well at the prices we would pay for those tasks being done.

So why don’t we trade with ants?

I claim that we don’t trade with ants because we can’t communicate with them. We can’t tell them what we’d like them to do, and can’t have them recognize that we would pay them if they did it. Which might be more than the language barrier. There might be a conceptual poverty. There might also be a lack of the memory and consistent identity that allows an ant to uphold commitments it made with me five minutes ago.

To get basic trade going, you might not need much of these things though. If we could only communicate that their all leaving our house immediately would prompt us to put a plate of honey in the garden for them and/or not slaughter them, then we would already be gaining from trade.

So it looks like the the AI-human relationship is importantly disanalogous to the human-ant relationship, because the big reason we don’t trade with ants will not apply to AI systems potentially trading with us: we can’t communicate with ants, AI can communicate with us.

(You might think ‘but the AI will be so far above us that it will think of itself as unable to communicate with us, in the same way that we can’t with the ants – we will be unable to conceive of most of its concepts’. It seems unlikely to me that one needs anything like the full palette of concepts available to the smarter creature to make productive trade. With ants, ‘go over there and we won’t kill you’ would do a lot, and it doesn’t involve concepts at the foggy pinnacle of human meaning-construction. The issue with ants is that we can’t communicate almost at all.)

But also: ants can actually do heaps of things we can’t, whereas (arguably) at some point that won’t be true for us relative to AI systems. (When we get human-level AI, will that AI also be ant level? Or will AI want to trade with ants for longer than it wants to trade with us? It can probably better figure out how to talk to ants.) However just because at some point AI systems will probably do everything humans do, doesn’t mean that this will happen on any particular timeline, e.g. the same one on which AI becomes ‘very powerful’. If the situation turns out similar to us and ants, we might expect that we continue to have a bunch of niche uses for a while.

In sum, for AI systems to be to humans as we are to ants, would be for us to be able to do many tasks better than AI, and for the AI systems to be willing to pay us grandly for them, but for them to be unable to tell us this, or even to warn us to get out of the way. Is this what AI will be like? No. AI will be able to communicate with us, though at some point we will be less useful to AI systems than ants could be to us if they could communicate.

But, you might argue, being totally unable to communicate makes one useless, even if one has skills that could be good if accessible through communication. So being unable to communicate is just a kind of being useless, and how we treat ants is an apt case study in treatment of powerless and useless creatures, even if the uselessness has an unusual cause. This seems sort of right, but a) being unable to communicate probably makes a creature more absolutely useless than if it just lacks skills, because even an unskilled creature is sometimes in a position to add value e.g. by moving out of the way instead of having to be killed, b) the corner-ness of the case of ant uselessness might make general intuitive implications carry over poorly to other cases, c) the fact that the ant situation can definitely not apply to us relative to AIs seems interesting, and d) it just kind of worries me that when people are thinking about this analogy with ants, they are imagining it all wrong in the details, even if the conclusion should be the same.

Also, there’s a thought that AI being as much more powerful than us as we are than ants implies a uselessness that makes extermination almost guaranteed. But ants, while extremely powerless, are only useless to us by an accident of signaling systems. And we know that problem won’t apply in the case of AI. Perhaps we should not expect to so easily become useless to AI systems, even supposing they take all power from humans.

Appendix: potentially valuable things things ants can do
--------------------------------------------------------

1. Clean, especially small loose particles or detachable substances, especially in cases that are very hard for humans to reach (e.g. floors, crevices, sticky jars in the kitchen, buildup from pipes while water is off, the outsides of tall buildings)
2. Chase away other insects
3. Pest control in agriculture (they have [already been used for this](https://en.wikipedia.org/wiki/Weaver_ant#In_agriculture) since about 400AD)
4. Surveillance and spying
5. Investigating hard to reach situations, underground or in walls for instance – e.g. see whether a pipe is leaking, or whether the foundation of a house is rotting, or whether there is smoke inside a wall
6. Surveil buildings for [smoke](https://www.goshen.edu/blogs/2014/05/10/ants-smoke-final-survey/#:~:text=So%2C%20today%20we%20spent%20several,the%20ground%20to%20save%20themselves.&text=As%20far%20as%201800m%20away,over%20a%20hundred%20per%20minute!)
7. Defend areas from invaders, e.g. buildings, cars ([some plants have coordinated with ants in this way](https://en.wikipedia.org/wiki/Vachellia_drepanolobium#Symbiosis_with_ants))
8. Sculpting/moving things at a very small scale
9. Building [house-size structures](https://youtu.be/lFg21x2sj-M?t=171) with intricate detailing.
10. Digging tunnels (e.g. instead of digging up your garden to lay a pipe, maybe ants could dig the hole, then a flexible pipe could be pushed through it)
11. Being used in medication (this already happens, but might happen better if we could communicate with them)
12. Participating in war (attack, guerilla attack, sabotage, intelligence)
13. Mending things at a small scale, e.g. delivering adhesive material to a crack in a pipe while the water is off
14. Surveillance of scents (including which direction a scent is coming from), e.g. drugs, explosives, diseases, people, microbes
15. Tending other small, useful organisms (‘Leafcutter ants (Atta and Acromyrmex) feed exclusively on a fungus that grows only within their colonies. They continually collect leaves which are taken to the colony, cut into tiny pieces and placed in fungal gardens.’[Wikipedia](https://en.wikipedia.org/wiki/Leafcutter_ant#Ant%E2%80%93fungus_mutualism): ‘Leaf cutter ants are sensitive enough to adapt to the fungi’s reaction to different plant material, apparently detecting chemical signals from the fungus. If a particular type of leaf is toxic to the fungus, the colony will no longer collect it…The fungi used by the higher attine ants no longer produce spores. These ants fully domesticated their fungal partner 15 million years ago, a process that took 30 million years to complete.[9] Their fungi produce nutritious and swollen hyphal tips (gongylidia) that grow in bundles called staphylae, to specifically feed the ants.’ ‘The ants in turn keep predators away from the aphids and will move them from one feeding location to another. When migrating to a new area, many colonies will take the aphids with them, to ensure a continued supply of honeydew.’ [Wikipedia](https://en.wikipedia.org/wiki/Ant#Relationships_with_other_organisms):’Myrmecophilous (ant-loving) caterpillars of the butterfly family Lycaenidae (e.g., blues, coppers, or hairstreaks) are herded by the ants, led to feeding areas in the daytime, and brought inside the ants’ nest at night. The caterpillars have a gland which secretes honeydew when the ants massage them.’’)
16. Measuring hard to access distances (they measure distance as they walk with an internal pedometer)
17. Killing plants (lemon ants [make](https://en.wikipedia.org/wiki/Ant#Relationships_with_other_organisms) ‘devil’s gardens’ by killing all plants other than ‘lemon ant trees’ in an area)
18. Producing and delivering nitrogen to plants (‘Isotopic labelling studies suggest that plants also obtain nitrogen from the ants.’ – [Wikipedia](https://en.wikipedia.org/wiki/Ant#Relationships_with_other_organisms))
19. Get out of our houses before we are driven to expend effort killing them, and similarly for all the other places ants conflict with humans (stinging, eating crops, ..)