2024-07-23

Positive visions for AI

This post was a collaboration with Florence Hinder

Reasons to make the positive case

Everyone who starts thinking about AI starts thinking big. Alan Turing predicted that machine intelligence would make humanity appear feeble in comparison. I. J. Good said that AI is the last invention that humanity ever needs to invent.

The AI safety movement started from Eliezer Yudkowsky and others on the SL4 mailing list discussing (and aiming for) an intelligence explosion and colonizing the universe. However, as the promise of AI has drawn nearer, visions for AI upsides have paradoxically shrunk. Within the field of AI safety, this is due to a combination of the “doomers” believing in very high existential risk and therefore focusing on trying to avoid imminent human extinction rather than achieving the upside, people working on policy not talking about sci-fi upsides to look less weird, and recent progress in AI driving the focus towards concrete machine learning research rather than aspirational visions of the future.

Both DeepMind and OpenAI were explicitly founded as moonshot AGI projects (“solve intelligence, and then use that to solve everything else” in the words of Demis Hassabis). Now DeepMind - sorry, Google DeepMind - has been eaten by the corporate machinery of Alphabet, and OpenAI is increasingly captured by profit and product considerations.

The torch of AI techno-optimism has moved on the e/acc movement. Their core message is correct: growth, innovation, and energy are very important, and almost no one puts enough emphasis on them. However, their claims to take radical futures seriously are belied by the fact that their visions of the future seem to stop at GenAI unicorns. They also seem to take the general usefulness of innovation not as just a robust trend, but as a law of nature, and so are remarkably incurious about the possibility of important exceptions. Their deeper ideology is in parts incoherent and inhuman. Instead of centering human well-being, they worship the “thermodynamic will of the universe”. “You cannot stop the acceleration”, argues their figurehead, so “[y]ou might as well embrace it” - hardly an inspiring humanist rallying cry.

In this piece, we want to paint a picture of the possible benefits of AI, without ignoring the risks or shying away from radical visions. Why not dream about the future you hope for? It’s important to consider the future you want rather than just the future you don’t. Otherwise, you might create your own unfortunate destiny. In the Greek myth about Oedipus, he was prophesied to kill his father, so his father ordered him to be killed, but he wasn’t and ended up being adopted. Years later he crossed his father on the road in his travels and killed him, as he had no idea who his father was. Oedipus’ father focusing on the bad path might have made the prophecy happen. If Oedipus' father hadn’t ordered him to be killed, he would have known who his father was and likely wouldn’t have killed him. 

When thinking about AI, if we only focus on the catastrophic future, we may cause it to become true by causing an increase in attention on this topic. Sam Altman, who is leading the way in AI capabilities, claimed to have gotten interested from arch-doomer Eliezer Yudkowsky. We may also neglect progress towards positive AI developments; some people think that even direct AI alignment research should not be published because it might speed up the creation of unaligned AI.


With modern AI, we might even get a very direct “self-fulfilling prophecy” effect: current AIs increasingly know that they are AIs, and make predictions about how to act based on their training data which includes everything we write about AI.

Benefits of AI

Since we think a large focus of AI is on what could go wrong, let’s think through what could go well starting from what’s most tangible and close to the current usage of AI to what the more distant future could hold.

  1. AI will do the mundane work
  2. Lowering the costs of coordination
  3. Spreading Intelligence
  4. AI can create more technology
  5. Increased technology, wealth and energy, correlate with life being good
  6. All of the above, and the wealth it creates, could allow people to self-actualise more

Already, AI advances mean that Claude has beocme very useful, and programmers are faster and better. But below we’ll cast a look towards the bigger picture and where this could take us.

AI will do the mundane work

First, there’s a lot of mundane mental work that humans currently have to do. Dealing with admin work, filing taxes, coordinating parcel returns -- these are not the things you will fondly be reminiscing about as you lie on your deathbed. Software has reduced the pain of dealing with such things, but not perfectly. In the future, you should be able to deal with all administrative work by specifying what you want to get done to an AI, and being consulted on decision points or any ambiguities in your preferences. Many CEOs or executives have personal assistants; AIs will mean that everyone will have access to this. 

What about mundane physical work, like washing the dishes and cleaning the toilets? Currently, robotics is bad. But there is no known fundamental obstacle to having good robotics. It seems mainly downstream of a lot of engineering and a lot of data collection. AI can help with both of those. The household robots that we’ve been waiting for could finally become a reality.

Of course, it is unclear whether AIs will first have a comparative advantage against humans in mundane or meaningful work. We’re already seeing that AI models are making massive strides in making art, way before they’re managing our inboxes for us. It may be that there is a transitional period where robotics is lagging but AIs are smarter-than-human, where the main economic value of humans is their hands rather than their brains.

Lowering the cost of coordination

With AI agents being able to negotiate with other AI agents, the cost of coordination is likely to dramatically drop (see here for related discussion). Examples of coordination are agreements between multiple parties, or searching through a large pool of people to match buyers or sellers, or employees and employers. Searching through large sets of people, doing complex negotiations, and the monitoring and enforcement of agreements all take lots of human time. AI could reduce the cost and time taken by such work. In addition to efficiency gains, new opportunities for coordination will open up that would have previously been too expensive.

Small-scale coordination

To give an example of this on the small scale of two individuals, say you are trying to search for a new job. Normally you can’t review every single job posting ever, and employers can’t review every person in the world to see if they want to reach out. However, an AI could filter that for the individual and another AI for the business, and the two AIs could have detailed negotiations with each other to find the best possible match. 

Coordination as a scarce resource

A lot of the current economy is a coordination platform; that’s the main product of each of Google, Uber, Amazon, and Facebook. Reducing the cost of searching for matches and trades should unlock at least as much mundane benefits and economic value as the tech platforms have.

Increased coordination may also reduce the need to group people into roles, hierarchies, and stereotypes. Right now, we need to put people into rigid structures (e.g. large organisations with departments like “HR” or “R&D”, or specific roles like “doctor” or “developer”) when coordinating a large group of people. In addition to upholding standards and enabling specialisation of labour, another reason for this is that people need to be legible to unintelligent processes, like binning of applicants by profession, or the CEO using an org chart to find out who to ask about a problem, or someone trying to buy some type of service. Humans can reach a much higher level of nuance when dealing with their friends and immediate colleagues. The cheap intelligence we get from AI might let us deal with the same level of nuance with a larger group of people than humans can themselves track. This means people may be able to be more unique and differentiated, while still being able to interface with society.

Large-scale Coordination

On a larger scale, increased coordination will also impact geopolitics. Say there are two countries fighting over land or resources. Both countries could have AI agents to negotiate with the other AI agents to search the space of possible deals and find an optimal compromise for both. They could also simulate a vast number of war scenarios to figure out what would happen; much conflict is about two sides disagreeing about who would win and resolving the uncertainty through a real-world test. This relies on three key abilities: the ability to negotiate cheaply, the ability to simulate outcomes, and the ability to stick to and enforce contracts. AI is likely to help with all three. This could reduce the incentives for traditional war, in that no human lives are needed to be lost because the outcome is already known and we can negotiate straight from that. We also know exactly what we are and are not willing to trade off which means it’s easier to optimise for the best compromise for everyone.

Spreading the intelligence

AI lets us spread the benefits of being smart more widely.

The benefits of intelligence are large. For example, this study estimates that a 1 standard deviation increase in intelligence increases your odds of self-assessed happiness by 11%. Now, part of this gain comes from intelligence being a positional good: you benefit from having more intelligence at your disposal than others, for example in competing for a fixed set of places. However, intelligence also has absolute benefits, since it lets you make better choices. And AI means you can convert energy into intelligence. Much as physical machines let the weak gain some of the benefits of (even superhuman) strength, AI might allow all humans to enjoy some of the benefits of being smart.

Concretely, this could have two forms. The first is that you could have AI advisors increase your ability to make plans or decisions, in the same way that - hypothetically - even a near-senile president might still make decent decisions with the help of their smart advisors. With AI, everyone could have access to comparable expert advisors. The effect may be even more dramatic than human advisors: the AI might be superhumanly smart, the AI might be more verifiably smart (a big problem in selecting smart advisors is that it can be hard to tell who is actually smart, especially if you are not), and if AIs are aligned successfully there may be less to worry about in trusting it than in trusting potentially-scheming human advisors. 

The second is AI tutoring. Human 1-1 tutoring boosts educational outcomes by 2 standard deviations (2 standard deviations above average is often considered the cutoff for “giftedness”). If AI tutoring is as good, that’s a big deal.

AI is the ultimate meta-technology

AI is special because it automates intelligence, and intelligence is what you need to build technology, including AI, creating a feedback loop. Some other previous technologies have boosted other technologies; for example, the printing press massively helped the accumulation of knowledge that led to the invention of many other technologies. But we have not before had a technology that could itself directly advance other technology. Such AI has been called PASTA (Process for Automating Scientific and Technological Advancement).

Positive feedback loops - whether self-improving AIs, nuclear reactions, epidemics, or human cultural evolution - are very powerful, so you should be wary of risks from them. Similarly, it is currently at best extremely unclear whether AIs that improve themselves could be controlled with current technology. We should be very cautious in using AI systems to improve themselves.

In the long run, however, most of the value of AI will likely come from their effects on technological progress, much like the next industrial revolution. We can imagine AIs slashing the cost and increasing the speed of science in every field, curing diseases and making entire new veins of technology available, in the same way that steam engines made entirely new veins of coal accessible.

In particular, AIs help de-risk one of the largest current risks to future human progress. One model of the feedback loop behind humanity’s progress in the past few centuries is that people led to ideas led to wealth led to food led to more people.

However, greater wealth no longer translates into more people. The world population, which was exponentially growing for much of the 19th and 20th centuries, is likely to be in decline by the end of the 21st century. This is likely to have negative consequences for the rate of innovation, and as discussed in the next section, a decline in productivity would likely have a negative impact on human wellbeing. However, if AIs start driving innovation, then we have a new feedback loop: wealth leads to energy leads to more AIs leads to ideas leads to wealth.

As long as this feedback loop does not decouple from the human economy and instead continues benefitting humans, this could help progress continue long into the future.

Wealth and energy are good

If you want humans to be well-off, one of the easiest things to do is give them more wealth and more energy. GDP per capita (on a log scale) has a 0.79 correlation with life satisfaction, and per-capita energy use (again on a log scale) has a 0.74 correlation with life satisfaction. Increased wealth and energy correlate with life satisfaction, and we should expect these trends to continue.

Above: GDP per capita (x-axis), energy use (y-axis), and life satisfaction (colour scale) for 142 countries. There are no poor countries with high energy use, and no high energy use countries that are poor. There are no countries with high average life satisfaction that are not high in both energy use and average GDP per capita. The axes are logarithmic, but since economic growth is exponential, countries should be able to make progress at a constant rate along the axis. Data source: Our World In Data (here, here, and here).

(It is true that energy use and economic growth have been increasingly decoupling in rich countries, due to services being more of the economy, and efficiency gains in energy use. However, the latter is effectively increasing the amount of useful energy that can be used - e.g. say the amount of energy needed to cook one meal is now enough to cook two meals, which is effectively the same as gaining more energy. However, efficiency effects are fundamentally limited because there is a physical limit, and also if demand is elastic then efficiency gains lead to increased energy use, meaning it doesn’t help the environment either. Ultimately, if you want to do more things in the physical world, you need more energy).

A wealthy, energy-rich society has many material benefits: plentiful food, advanced medicine, high redistributive spending becomes feasible, and great choice and personal freedom through specialisation of labour and high spending power. A wealthy and energy-rich society also has some important subtler benefits. Poverty and resource constraints sharpen conflict. Economic growth is intimately linked to tolerance and liberalism, by weakening the cultural status and clout of zero-sum strategies like conflict and politicking.

One clear historic example of how increases in energy correlated with improved quality of life was in the industrial revolution, arguably the best and most important thing that ever happened. Before it, trends in human wellbeing seemed either stagnant, fluctuating, or very slow, and after it, all the variables for which we can find good long-term series that are related to human well-being shoot upwards.

Above: variables correlated with human well-being over time. Source:  Luke Muehlhauser

Therefore, it’s worth keeping in mind that boosting energy and wealth is good, actually. And the most powerful way to do that is through inventing new technologies that let us use energy to serve our needs.

The heart of the industrial revolution was replacing part of human manual labour with something cheaper and more powerful. AI that replaces large parts of human mental labour with something cheaper and more powerful should be expected to be similarly transformative. Whether it is a good or bad transformation seems more uncertain. We are lucky that industrialisation happened to make national power very tightly tied to having a large, educated, and prosperous middle class; it is unclear what is the winning strategy in an AI economy. We are also lucky that the powerful totalitarian states enabled by industrial technology have not triumphed so far, and they might get further boosts from AI. Automating mental labour also involves the automation of decision-making, and handing over decision-making to the machines is handing over power to machines, which is more risky than handing the manual labour to them. But if we can safely control our AI systems and engineer good incentives for the resulting society, we could get another leap in human welfare.

Self actualisation

Now say we’ve had a leap in innovation and energy through Transformative AI (TAI) and we’ve also reached a post scarcity world. What happens now? Humans have had all their basic needs met, most jobs are automated,  but what do people spend their time actually doing?

Maslow’s Hierarchy

Maslow’s hierachy of needs is a framework of understanding human needs and drivers for human behaviour. Maslow suggested that in most scenarios people need to mostly satisfy one level before being able to focus on higher-level needs. 

The top level of the hierachy is self-actualisation. The peak of human experience is something that few can currently reach - but maybe everyone could get there.

There is a possible path the world takes in which all humans can reach self-actualisation. With increases in technology & wealth, such as with TAI and a Universal Basic Income (UBI), we would be able to provide the basic needs of food, water, shelter, and clothing for all humans, enabling people to easily meet their basic needs. Humans can now spend more time on the things they want, for example moving up through Maslow’s hierarchy to focusing on increasing love and belonging, self-esteem and self-actualization.

Say you are in a post scarcity world, what would you do if you didn’t have to work?

Would you be spending time with loved ones, engaging in social activities that provide a sense of connection and belonging, self-esteem? Would it be honing your craft and becoming an expert in a particular field? Or would you spend the whole time scrolling on your phone?

Say hypothetically a wealthy billionaire gave you a grant to work on anything you wanted, would you be happy with having the complete freedom to spend your time as you wished?

Often people assume that others will be unhappy with this world, but would you? There is a cognitive bias where people tend to judge themselves as happier than their peers, which could nudge you to think people would be less happy in this world, even if you would enjoy this. 

In this post-scarcity world, humans could spend more time on creative pursuits such as art, music, and any other hobbies – not with the goal of making money, but to reach self-actualisation. 

With AI being better than humans in every dimension, AI can produce the best art in the world, but there is intrinsic value in honing your craft, improving at art or expressing your feelings through it, in and of itself. The vast majority of art is not created to be the best art in the world but for the journey itself. A child that paints a finger painting and the parent who puts it on the wall does not think “my child’s art is better than Van Gogh’s”. Instead, they feel a sense of excitement about the progress their child has made and the creative expression the child has produced. 

Another example is the Olympic games. Nobody needs to win the olympic games to survive, but it lets people express pride in their country, hone their craft, attain status, and so on. But the actual task is just a game, a social construct. More and more tasks will look like social constructs and games we create to challenge each other.

Examples of post-scarcity scenes 

Since this is quite theoretical, let's consider examples where we’ve had “post-scarcity” microcosms to explore. 

The French Bourgeoisie 

The French leisure class, or bourgeoisie, were a class of wealthy elite that emerged in 16th century France. Many had enough money to pursue endeavours like refining their taste in arts and culture. Salon culture was a cornerstone of bourgeoisie social life. Gatherings featuring discussions on literature, art, politics and philosophy. 

Upper Class in the Victorian Era

The upper class in the Victorian era enjoyed a variety of leisure activities that reflected their wealth, status and values. They attended social events and balls, fox hunting and other sports, theater and opera, art and literature, travel, tea parties and social visits, gardening and horticulture, charitable work and philanthropy. Several undertook serious pursuits in science or art. 

Burning Man

Burning Man is an annual festival where people take all the basic things you need with you for a week of living in the desert:food, water, shelter. People have a week to create a new community or city that is a temporary microcosm of a post-scarcity world. They pursue artistic endeavours and creative expression, music, dance and connecting with others. People often talk about Burning Man events being some of the best experiences of their lives. 

Successful Startup Founders in The Bay Area

In San Francisco, there is a crossover with hippie culture and tech, and many people with excess wealth and resources, resulting in many looking for more in life. They try to reach self actualisation, by pursuing many arts and creative pursuits. Hippie movements often encourage communal living, and a sense of connection with those around you. Many may raise eyebrows at the lifestyles of some such people, but it’s hard to claim that it’s a fundamentally bad existence.

More pessimistic views about humans?

It is true that not all cultural tendencies in a post-scarcity world would be positive. In particular, humans have a remarkable ability to have extremely tough and all-consuming social status games, seemingly especially in environments where other needs are met. See for example this book review about the cut-throat social scene of upper-class Manhattan women or this one about the bland sameness and wastefulness of nightlife, or this book review that ends up concluding that the trajectory of human social evolution is one long arc from prehistoric gossip traps to internet gossip traps, with liberal institutions just a passing phase.

But the liberal humanist attitude here is to let humans be humans. Yes, they will have petty dramas and competitions, but if that is what they want, who is to tell them no? And they will also have joy and love.

Would a post-scarcity world have meaning? Adversity is one of the greatest sources of meaning. Consider D-Day, when hundreds of thousands of soldiers got together to charge up a beach under machine-gun fire to liberate a continent from Nazi rule. Or consider a poor parent of four working three jobs to make ends meet. There are few greater sources of meaning. But adversity can be meaningful while involving less suffering and loss. A good future will be shallower, in a sense, but that is a good thing.

Finally, it is unclear if we would get a happy world, even if we had the technology for post-scarcity, because of politics and conflict. We will discuss this later.

Radical improvements

AI might also help with radical but necessary improvements to the human condition.

People die. It is a moral tragedy when people are forced to die against their will, as happens to over 50 million people per year. Medicine is making progress against many causes of death and disability; in the limit it can cure all of them. We should reach that limit as fast as possible, and AI can likely help accelerate the research and deployment of solutions.

One of the greatest inequalities in the world is inequality in intelligence. Some people struggle to perform in simple jobs, while others (well, at least one) are John von Neumann. In the short term, AI might help by making cognitively demanding tasks more accessible to people through AI tutors and AI copilots. In the longer term, AI might help us enhance human intelligence, through brain-AI integration or new medical technology.

Reasons to worry

Though there are many potential upsides for AI and AGI as argued in this post, that doesn’t mean there aren’t risks. 

The plausible risks of AI go all the way to human extinction, meaning this shouldn’t be taken lightly. Since this piece is focused on the upside risk, not the downside risk, we will not argue this point in depth, but it is worth revisiting briefly.

Existential risk from AI is a serious concern

It is intuitive that AI is risky.

First, creating something smarter, faster, and more capable than humans is obviously risky, since you need to very precisely either control it (i.e. stop it from doing things you don’t like) or align it (i.e. make it always try to do what you would want it to do). Both the control and alignment problem for AIs still have unsolved technical challenges. And that’s assuming that AI is in the right hands.

Second, even if the AIs remain in our control, they are likely to be as transformative as the industrial revolution. Eighteenth-century European monarchs would’ve found it hard to imagine how the steam engine could challenge their power, but the social changes that were in part a result of them eventually wrested all their powers away. In the modern world, a lot of power depends on large educated workforces of humans, whereas sufficiently strong AGI might decorrelate power and humans, decreasing the incentive to have people be educated and prosperous - or to have people around at all.

Apart from object-level arguments, consider too the seriousness with which the AI doomsday is discussed. Many top researchers and all top AI lab CEOs have signed a statement saying “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war”. Nuclear war and pandemics are the only other cases where similarly serious predictions have been made by a similarly serious set of people (though arguably climate change is close: the science on the effects is more established and certain, but while catastrophe is more likely, literal human extinction from it is much less likely).

Side-effects of non-existentially-bad AI might be large

Consider the internet, a widely-successful technology with a lot of benefits. There are credible claims that the internet is responsible for harms ranging from massively increased depression rates among teenagers to political polarisation to widespread productivity loss through addiction and distraction.

In the same way, the success of AI might lead to bad side effects, even if all the existential risks are avoided.

For example, AI could replace human connection. Human friends and partners might increasingly be replaced with AIs. However bad it was in other ways, at least on pre-AI social media you at least interacted with humans (or simple algorithms), but with AIs it’s possible to have what looks like deep emotional relationships. Just look at the Replika subreddit from a year ago when they changed the algorithm to only allow “PG-rated interactions”. Many users were upset. The film “Her” doesn’t seem far off, as Sam Altman acknowledges. Such relationships give the human much more safety and control than in human relationships, which might both be very attractive to humans, while also excessively coddling them. Given that much human happiness and meaning comes from human relationships and bonding, widespread AI substitution of them could mean the destruction of a large part of all human wellbeing and meaning in the world. On a more prosaic level, society might atomise into individuals hoarding compute credits to spend on running their AI companions without connecting with other humans, with severe effects on society’s functioning, or humans might stop having children and human populations might crash. Humanity has flourished through collaboration and socialisation. If we use AIs to replace this in an overly thoughtless way, the fabric of society could crumble.

Apart from being superhuman at forming relationships with humans, AIs might be superhuman at persuasion. We can imagine AIs producing the vast majority of content that people consume. We can imagine a totalitarian world where the governments with the greatest compute resources can dominate the conversation forever. Instead of humans having ideas and sometimes persuading other humans to adopt them, driving social progress, any human-generated ideas might be swamped by a greater quantity of superhumanly persuasive counter-arguments that support the status quo. We can also imagine a dystopian decentralised world. Already, many online memes (in Dawkins’s original sense of the word) are maladaptive, spreading not by having good effects on their hosts but by being incredibly good at spreading from person to person. AI might make us much better at searching the space of ideas for the most viral ones. Ideas that aren’t maximally viral might be outcompeted. Eventually, our institutions could become mere puppets that serve as viral hosts for the most transmissive memes, as part of an endless tug-of-war where AI-generated memes compete to compel humans to spread them.

Seems bad.

Not good nor bad, but some third thing.

Many debates turn into mood affiliation debates. Are guns bad? Is more government good? But remember: politics is the mindkiller. Navigating a complicated world requires more than the ability to stick the label “good” or “bad” on entire domains. If you were seated in the control room of a nuclear power station, you wouldn’t ask yourself: uranium, good or bad? Instead, you want to steer towards the small set of states where the reaction is perched between dying out and exploding, while generating useful clean power.

We’ve also seen again and again that technology and social change have strong effects on each other, and these are often hard to predict. We’ve discussed how industrial technology may have led to democracy. There is serious academic debate about whether the stirrup caused feudalism, or whether the Black Death was a driver of European liberalism, or whether social media was a significant cause of the Arab Spring. The birth control pill was a major influence of the sexual revolution, and the printing press helped the Protestant Reformation. Often, the consequences of a new technology are some obvious direct benefits, some obvious direct harms, and the shifting of some vast social equilibrium that ends up forever reshaping the world in some way no one saw coming. So far we’ve clearly ended up ahead on net, and maybe that will continue.

Humanity has spent over a hundred thousand years riding a feedback loop of accumulating cultural evolution. Over the past few hundred, the industrial revolution boosted the technological progress feedback loop. Human wellbeing has skyrocketed, though along the way we’ve had - and are continuing to have - close calls with nuclear war, totalitarianism, and environmental issues. We’ve had a healthy dose of luck, including in generalities like the incentive structures of industrial economics and specifics like the heroism of Stanislav Petrov. But we’ve also had an enormous amount of human effort and ingenuity spent on trying to chart a good path for civilization, from solar panel subsidies to the Allies winning World War 2.

For most of this time, the direction of the arrow of progress has been obvious. The miseries of poverty and the horrors of close-up totalitarianism are very powerful driving forces after all. And while both continue ravaging the world, developed countries have in many ways gotten complacent. There are fewer obvious areas of improvement for those lucky enough to enjoy a life of affluence in the developed world. But the future could be much better still.

Know where to aim

We think it’s important to have a target of what to aim for. We need to dream about the future we want. A strong culture needs a story of what it is driving towards, and humanity needs a compelling vision of how our future turns out well so we can work together to create the future we all want. AI seems like the biggest upcoming opportunity and risk. We hope we can avoid the risks, and realise the positive vision presented here, together with a hundred other things we can’t yet imagine.


See LessWrong for additional comments & discussion.

2024-01-08

A model of research skill

~4k words (20 minutes)

Doing research means answering questions no one yet knows the answer to. Lots of impactful projects are downstream of being good at this. A good first step is to have a model for what the hard parts of research skill are.

Two failure modes

There are two opposing failure modes you can fall into when thinking about research skill.

The first is the deferential one. Research skill is this amorphous complicated things, so the only way to be sure you have it is to spend years developing it within some ossified ancient bureaucracy and then have someone in a funny hat hand you a piece of paper (bonus points for Latin being involved).

The second is the hubristic one. You want to do, say, AI alignment research. This involves thinking hard, maybe writing some code, maybe doing some maths, and then writing up your results. You’re good at thinking - after all, you read the Sequences, like, 1.5 times. You can code. You did a STEM undergrad. And writing? Pffft, you’ve been doing that since kindergarten!

I think there’s a lot to be said for hubris. Skills can often be learned well by colliding hard with reality in unstructured ways. Good coders are famously often self-taught. The venture capitalists who thought that management experience and a solid business background are needed to build a billion-dollar company are now mostly extinct.

It’s less clear that research works like this, though. I’ve often heard it said that it’s rare for a researcher to do great work without having been mentored by someone who was themselves a great researcher. Exceptions exist and I’m sceptical that any good statistics exist on this point. However, this is the sort of hearsay an aspiring researcher should pay attention to. It also seems like the feedback signal in research is worse than in programming or startups, which makes it harder to learn.

Methodology, except “methodology” is too fancy a word

To answer this question, and steer between deferential confusion and hubristic over-simplicity, I interviewed people who had done good research to try to understand their models of research skill. I also read a lot of blog posts. Specifically, I wanted to understand what about research a bright, agentic, technical person trying to learn at high speed would likely fail at and either not realise or not be able to fix quickly.

I did structured interviews with Neel Nanda (Google DeepMind; grokking), Lauro Langosco (Krueger Labgoal misgeneralisation), and one other. I also learned a lot from unstructured conversations with Ferenc HuszarDmitrii KrasheninnikovSören MindermannOwain Evans, and several others. I then ~procrastinated on this project for 6 months~ touched grass and formed inside views by doing the MATS research program under the mentorship of Owain Evans. I owe a lot to the people I spoke to and their willingness to give their time and takes, but my interpretation and model should not taken as one they would necessarily endorse.

My own first-hand research experience consists mainly of a research-oriented CS (i.e. ML) master’s degree, followed by working as a full-time researcher for 6 months and counting. There are many who have better inside views than I do on this topic.

The Big Three

In summary:

  1. There are a lot of ways reality could be (i.e. hypotheses), and a lot of possible experiment designs. You want to avoid brute-forcing your way through these large spaces as much as possible, and instead be good at picking likely-true hypotheses or informative experiments. Being good at this is called research taste, and it’s largely an intuitive thing that develops over a lot of time spent engaging with a field.
  2. Once you have some bits of evidence from your experiment, it’s easy to over-interpret them (perhaps you interpret them as more bits than they actually are, or perhaps you were failing to consider how large hypothesis space is to start with). To counteract this, you need sufficient paranoia about your results, which mainly just takes careful and creative thought, and good epistemics.
  3. Finally, you need to communicate your results to transfer those bits of evidence into other people’s heads, because we live in a society.

Taste

Empirically, it seems that a lot of the value of senior researchers is a better sense of which questions are important to tackle, and better judgement for what angles of attack will work. For example, good PhD students often say that even if they’re generally as technically competent as their adviser and read a lot of papers, their adviser has much better quick judgements about whether something is a promising direction.

When I was working on my master’s thesis, I had several moments where I was working through some maths and get stuck. I’d go to one of my supervisors, a PhD student, and they’d have some ideas on angles of attack that I hadn’t thought of. We’d work on it for an hour and make more progress than I had in several hours on my own. Then I’d go to another one of my supervisors, a professor, and in fifteen minutes they’d have tried something that worked. Part of this is experience making you faster at crunching through derivations, and knowing things like helpful identities or methods. But the biggest difference seemed to be a good gut feeling for what the most promising angle or next step is.

I think the fundamental driver of this effect is dealing with large spaces: there are many possible ways reality could be (John Wentworth talks about this here), and many possible things you could try, and even being slightly better at honing in on the right things helps a lot. Let’s say you’re trying to prove a theorem that takes 4 steps to prove. If you have a 80% chance of picking the right move at each step, you’ll have a 41% chance of success per attempt. If that chance is 60%, you’ll have a 13% chance – over 3 times less. If you’re trying to find the right hypothesis within some hypothesis space, and you’ve already managed to cut down the entropy of your probability distribution over hypotheses to 10 bits, you’ll be able to narrow down to the correct hypothesis faster and with fewer bits than someone whose entropy is 15 bits (and who’s search space is therefore effectively 25 = 32 times as large). Of course, you’re rarely chasing down just a single hypothesis in a defined hypothesis class. But if you’re constantly 5 extra bits of evidence ahead compared to someone in what you’ve incorporated into your beliefs, you’ll make weirdly accurate guesses from their perspective.

Why does research taste seem to correlate so strongly with experience? I think it’s because the bottleneck is seeing and integrating evidence into your (both explicit and intuitive) world models. No one is close to having integrated all empirical evidence that exists, and new evidence keeps accumulating, so returns from reading and seeing more keep going. (In addition to literal experiments, I count things like “doing a thousand maths problems in this area of maths” as “empirical” evidence for your intuitions about which approaches work; I assume this gets distilled into half-conscious intuitions that your brain can then use when faced with similar problems in the future)

This suggests that the way to speed-run getting research taste is to see lots of evidence about research ideas failing or succeeding. To do this, you could:

  1. Have your own research ideas, and run experiments to test them. The feedback quality is theoretically ideal, since reality does not lie (but may be constrained by what experiments you can realistically run, and a lack of the paranoia that I talk about next). The main disadvantage is that this is often slow and/or expensive.
  2. Read papers to see whether other people’s research ideas succeeded or failed. This is prone to several problems:
    1. Biases: in theory, published papers are drawn from the set of ideas that ended up working, so you might not see negative samples (which is bad for learning). In practice, paper creation and selection processes are imperfect, so you might see lots of bad or poorly-communicated ones.
    2. Passivity: it’s easy to fool yourself into thinking you would’ve guessed the paper ideas beforehand. Active reading strategies could help; for example, read only the paper’s motivation section and write down what experiment you’d design to test it, and then read only the methodology section and write down a guess about the results.
  3. Ask someone more experienced than you to rate your ideas. A mentor’s feedback is not as good as reality’s, but you can get it a lot faster (at least in theory). The speed up is huge: a big ML experiment might take a month to set up and run, but you can probably get detailed feedback on 10 ideas in an hour of conversation. This is a ~7000x speedup. I suspect a lot of the value of research mentoring lies here: an enormous amount of predictable failures or inefficiently targeted ideas can be skipped or honed into better ones, before you spend time running the expensive test of actually checking with reality. (If true, this would imply that the value of research mentorship is higher whenever feedback loops are worse.)

Chris Olah has a list of suggestions for research taste exercises (number 1 is essentially the last point on my list above).

Research taste takes the most time to develop, and seems to explain the largest part of the performance gap between junior and senior researchers. It is therefore the single most important thing to focus on developing.

(If taste is so important, why does research output not increase monotonically with age in STEM fields? The scary biological explanation is that fluid intelligence (or energy or …) starts dropping at some age, and this decreases your ability to execute on maths/code, even assuming your research taste is constant or improving. Alternatively, hours used on deep technical work might tend to decline with advanced career stages.)

Paranoia

I heard several people saying that junior researchers will sometimes jump to conclusions, or interpret their evidence as saying more than it actually does. My instinctive reaction to this is: “wait, but surely if you just creatively brainstorm the ways the evidence might be misleading, and take these into account in making your conclusions (or are industrious about running additional experiments to check them), you can just avoid this failure mode?” The average answer I got was that yes, this seems true, and indeed many people either only need one peer review cycle to internalise this mindset, or pretty much get it from the start. Therefore, I’m almost tempted to chuck this category off this list, and onto the list of less crucial things where “be generally competent and strategic” will sort you out in a reasonable amount of time. However, two things hold me back.

First, confirmation bias is a strong thing, and it seems helpful to wave a big red sign saying “WARNING: you may be about to experience confirmation bias”.

Second, I think this is one of the cases where the level of paranoia required is sometimes more than you expect, even after you expect it will be high. John Wentworth puts this best in You Are Not Measuring What You Think You Are Measuring, which you should go read right now. There are more confounders and weird effects than are dreamt of in your philosophies.

A few people mentioned going through the peer review process as being a particularly helpful thing for developing paranoia.

Communication

I started out sceptical about the difficulty of research-specific communication, above and beyond general good writing. However, I was eventually persuaded that yes, research-specific communication skills exist and are important.

First, if research has impact, it is through communication. Rob Miles once said (at a talk) something along the lines of: “if you’re trying to ensure positive AGI outcomes through technical work, and you think that you are not going to be one of the people who literally writes the code for it or is in the room when it’s turned on, your path to impact lies through telling other people about your technical ideas.” (This generalises: if you want to drive good policy through your research and you’re not literally writing it …, etc.) So you should expect good communication to be a force multiplier applied on top of everything else, and therefore very important.

Secondly, research is often not communicated well. On the smaller scale, Steven Pinker moans endlessly – and with good reason – about academic prose (my particular pet peeve is the endemic utilisation of the word “utilise” in ML papers.). On the larger scale, entire research agendas can get ignored because the key ideas aren’t communicated in a sufficiently clear and legible way.

I don’t know what’s the best way to speed-run getting good at research communication. Maybe read Pinker to make sure you’re not making predictable mistakes in general writing. I’ve heard that experienced researchers are often good at writing papers, so maybe seek feedback from any you know (but don’t internalise the things they say that are about goodharting for paper acceptance). With papers, understand how papers are read. Some sources of research-specific communication difficulty I can see are (a) the unusually high need for precision (especially in papers), and (b) communicating the intuitive, high-context, and often unverbalised-by-default world models that guide your research taste (especially when talking about research agendas).

Other points

  • Having a research problem is not enough. You need an angle of attack.
    • Richard Feynman once said something like: keep a set of open problems in your head. Whenever you discover a new tool (e.g. a new method), run through this list of problems and see if you can apply it. I think this can also be extended to new facts; whenever you hear about a discovery, run through a list of open questions and see how you should update.
    • Hamming says something similar in You and your research: “Most great scientists know many important problems. They have something between 10 and 20 important problems for which they are looking for an attack.”
  • Research requires a large combination of things to go right. Often, someone will be good at a few of them but not all of them.
    • A sample list might be:
      • generating good ideas
      • picking good ideas (= research taste)
      • iterate rapidly to get empirical feedback
      • interpreting your results right (paranoia)
      • communicating your findings
    • If success is a product of either sufficiently many variables or of normally distributed variables, the distribution of success should be log-normal, and therefore fairly heavy-tailed. And yes, research is heavy-tailed. Dan Hendrycks and Thomas Woodside claim that while there may be 10x engineers, there are 1000x researchers. This seems true.
      • However, this also means that not being the best at one of the component skills does not doom your ability to still have a really good product across categories.
  • Ideas from other fields are often worth stealing. There exist standardised pipelines to produce people who are experts in X for many different X, but far less so to produce people who are experts in both X and some other Y. Expect many people in X to miss out on ideas in Y (though remember that not all Y are relevant).
  • Research involves infrequent and uncertain feedback. Motivation is important and can be hard. Grad students are notorious for having bad mental health. A big chunk of this is due to the insanities of academia rather than research itself. However, startups are somewhat analogous to research (high-risk, difficult, often ambiguous structure), lack institutionalised insanity, and are also acknowledged to be mentally tough.
    • The most powerful and universally-applicable hack to make something not suck for a human is for that human to do it together with other humans. Also, more humans = more brains.
  • Getting new research ideas is often not a particularly big-brained process. Once I had the impression that most research ideas would come from explicitly thinking hard about research ideas, and generating fancy ideas would be a major bottleneck. However, I’ve found that many ideas come with surprisingly little effort, with a feeling of “well, if I want X, the type of thing I should do is probably Y”. Whiteboarding with other people is also great.
    • This is not to say that idea generation isn’t helped by actively brainstorming hard. Just that it’s not the only, or even majority, source of ideas.
    • The feeling of ideas being rare is often a newbie phase. You should (and very likely will) pass over it quickly if you’re engaging with a field. John Wentworth has a good post on the topic. I have personally experienced an increase in concrete research ideas, and much greater willingness to discard ideas, after going through a few I’ve felt excited by.
    • When you look at a field from afar, you see a smooth shape of big topics and abstractions. This makes it easy to feel that everything is done. Once you’re actually at the frontier, you invariably discover that it’s full of holes, with many simple questions that don’t have answers.
  • There’s great benefit to an idea being the top thing in your mind.
  • When in doubt, log more. Easily being able to run more analyses is good. At some point you will think to yourself something like “huh, I wonder if thing X13 had an effect, I’ll run the statistics”, and then either thank yourself because you logged the value of X13 in your experiments, or facepalm because you didn’t.
  • Tolerate the appearance of stupidity (in yourself and others). Research is an intellectual domain, and humans are status-obsessed monkeys. Humans doing research therefore often feel like they need to appear smart. This can lead to a type of wishful thinking where you hear some idea and try to delude yourself (and others) into thinking you understand it immediately, without actually knowing how it bottoms out into concrete things. Remember that any valid idea or chain of reasoning decomposes into simple pieces. Allow yourself to think about the simple things, and ask questions about them.
    • There is an anecdote about Niels Bohr (related by George Gamow and quoted here): “Many a time, a visiting young physicist (most physicists visiting Copenhagen were young) would deliver a brilliant talk about his recent calculations on some intricate problem of the quantum theory. Everybody in the audience would understand the argument quite clearly, but Bohr wouldn’t. So everybody would start to explain to Bohr the simple point he had missed, and in the resulting turmoil everybody would stop understanding anything. Finally, after a considerable period of time, Bohr would begin to understand, and it would turn out that what he understood about the problem presented by the visitor was quite different from what the visitor meant, and was correct, while the visitor’s interpretation was wrong.”
  • “Real artists researchers ship”. Like in anything else, iteration speed really matters.
    • Sometimes high iteration speed means schlepping. You should not hesitate to schlep. The deep learning revolution started when some people wrote a lot of low-level CUDA code to get a neural network to run on a GPU. I once reflected on why my experiments were going slower than I hoped, and realised a mental ick for hacky code was making me go about things in a complex roundabout way. I spent a few hours writing ugly code in Jupyter notebooks, got results, and moved on. Researchers are notorious for writing bad code, but there are reasons (apart from laziness and lack of experience) why the style of researcher code is sometimes different from standards of good software.
    • The most important thing is doing informative things that make you collide with reality at a high rate, but being even slightly strategic will give great improvements on even that. Jacob Steinhardt gives good advice about this in Research as a Stochastic Decision Process. In particular, start with the thing that is most informative per unit time (rather than e.g. the easiest to do).

Good things to read on research skill

(I have already linked to some of these above.)

2023-06-04

A Disneyland Without Children

The spaceship swung into orbit around the blue-grey planet with a final burn of its engines. Compared to the distance they had travelled, the world, now only some four hundred kilometres below and filling up one hemisphere of the sky, was practically within reach. But Alice was no less confused.

“Well?” she asked.

Charlie stared thoughtfully at the world slowly rotating underneath their feet, oceans glinting in the sunlight. “It looks lickable”, he said.

“We have a task”, Alice said, trying to sound gentle. Spaceflight was hard. Organic life was not designed for it. But their mission was critical, they needed to move fast, and Charlie, for all his quirks, would need to be focused.

“What’s a few minutes when it will take years for anything we discover to be known back home?” Charlie asked.

“No licking”, Alice said.

Charlie rolled his eyes, then refocused them on the surface of the planet below. They were just crossing the coast of one of the larger continents. Blue water was giving way to grey land.

“Look at the texture”, Charlie said. They had seen it from far away with telescopes, but there was something different about seeing it with their bare eyes. Most of the land surface of the planet was like a rug of fine grey mesh. If there had been lights, Alice would have guessed the entire planet’s land was one sprawling city, but as far as their instruments could tell, the world had no artificial lighting.

As far as they could tell, the world also had no radio. They had broadcast messages at every frequency they could, and in desperation even by using their engines to flash a message during their deceleration burn. No response had come.

Alice pulled up one of the telescope feeds on the computer to look closer at the surface. She saw grey rectangular slabs, typically several hundred metres on a side, with wide roads running between them. The pattern was not perfect - sometimes it was irregular, and sometimes there were smaller features too. Some of the smaller ones moved.

“Are they factories?” Charlie asked.

“I’d guess so”, Alice said, watching on the telescope feed as a steady stream of rectangular moving objects, each about ten metres long, slid along a street. Another such stream was moving along an intersecting street, and it looked like they would crash at the intersection, but the timing and spacing was such that vehicles from one stream crossed the road just as there were gaps in vehicles along the other stream.

“A planet covered by factories, then”, Charlie said. “With no one home to turn the lights on.”

“I want to see what they’re making”, Alice said.

-

All through the atmospheric entry of their first drone package, Alice sat tight in her seat and clenched and unclenched her hands. So far all they had done was passive observation or broadcasting. A chunky piece of hardware tracing a streak of red-hot plasma behind it was a much louder knock. She imagined alien jet fighters scrambling to destroy their drones, and some space defence mechanism activating to burn their ship.

The image she saw was a jittery camera feed, showing the black back of the heatshield, the grey skin of the drone package, and a sliver of blue sky. It shook violently as the two halves of the heatshield detached from each other and then the drone package, tumbling off in opposite directions. Land became visible, kilometres below, the grey blocks of the buildings tiny like children’s blocks but still visibly three-dimensional, casting shadows and moving as the drone package continued falling.

The three drones tested their engines, and for a moment flew - or at least slowed their descent - in an ungainly joint configuration, before breaking off from each other and spreading their wings to the fullest. The feed showed the other two drones veering off into the distance on wide narrow wings, and then the view pulled up as the nose of the drone lifted from near-vertical to horizontal.

“Oops, looks like we have company”, Charlie said. He had been tapping away at some other screens while Alice watched the drone deployment sequence.

Alice jumped up from her seat. “What?”

“Our company is … a self-referential joke!”

Alice resisted the temptation to say anything and instead sunk back into her seat. On her monitor, the grey blocks continued slowly moving below the drone. She tapped her foot against the ground.

“Actually though”, Charlie said. “We’re not the only ones in orbit around this planet.”

“What else is orbiting? Has your sense of shame finally caught up with you and joined us?”

“Looks like satellites. Far above us, though. Can you guess how far?”

“I’d guess approximately the distance between you and maturity, so … five light-years?”

Charlie ignored her. “Exactly geostationary altitude”, he said, grinning. The grin was like some platonic ideal of intellectual excitement; too pure for Alice’s annoyance to stay with her, or for her to feel scared about the implications.

“But nothing in lower orbits?” Alice asked.

“No”, Charlie said. “Someone clearly put them there; stuff doesn’t end up at exactly geostationary altitude unless someone deliberately flies a communications or GPS satellite there. Now I can’t be entirely sure that the geostationary satellites are completely dead, but I’d guess that they are.”

“Like everything else”, Alice said, but even as she said so she caught sight of a long trail of vehicles making its way along one of the roads. There was something more real about seeing them on the drone feed.

“Maybe this is just a mining outpost”, Charlie said. “Big rocket launch to blast out a billion tons of ore to god-knows-where, once a year.”

“Or maybe they’re hiding underground or in the oceans”, Alice said.

“Let’s get one of the drones to drop a probe into the oceans. I’ll send one of our initial trio over to the nearest one, it’s only a few hundred kilometres away”, Charlie said.

“Sure”, Alice said.

They split the work of flying the drones, two of them mapping out more and more of the Great Grey Grid (as Alice took to calling it in her head), and one flying over the planet’s largest ocean.

Even the oceans were mostly a barren grey waste. Not empty, though. They did eventually see a few small scaly fish-like creatures that stared at their environment with uncomprehending eyes. Alien life. A young Alice would have been ecstatic. But now she was on a mission, and her inability to figure out what had happened on this planet annoyed her.

In addition to the ocean probe, they had rovers they could send crawling along the ground. Sometimes the doors of the square buildings were open, and Alice would drive a rover past one opening. Most seemed to either be warehouses of stacked crates, or then there would be some kind of automated assembly line of skeletal grey robot arms and moving conveyor belts. A few seemed to place more barriers between the open air and their contents; what went on there, the rovers did not see.

The first time Alice tried to steer a rover into a building, it got run over by a departing convoy of vehicles. The vehicles were rectangular in shape but with an aerodynamic head, with three wheels on each side. Based on their dimensions, she could easily imagine one weighing ten or twenty tons. The rover had no chance.

“Finally!” Charlie had said. “We get to fight these aliens.”

But there was no fight. It seemed like it had been a pure accident, without any hint of malice. The grey vehicles moved and stopped on some schedule of their own, and for all Alice knew they were not just insensitive beasts but blind and dumb ones too.

The next rover got in, quickly scooting through the side of the entrance and then off to one side, out of path of the grey vehicles. It wandered the building on its own, headlights turned on in the otherwise-dark building to bring back a video stream of an assembly line brooded over by those same skeletal hands they had glimpsed from outside. Black plastic beads came in by the million on the grey vehicles. A small thin arm with a spike on the end punctured a few holes on one side, and using these holes two of the black beads were sown onto an amorphous plushy shape. The shape got appendages, were covered with a layer of fluff, and the entire thing became a cheerful purple when it passed through an opaque box with pipes leading into it. It looked like a child’s impression of a hairy four-legged creature with black beady eyes above a long snout. A toy, but for who?

The conveyor belt took an endless line of those fake creatures past the rover’s camera at the end of the assembly line. Alice watched them go, one by one, and fall onto the open back of a grey vehicle. It felt like each and every one made eye contact with her, beady black eyes glinting in the light. She watched for a long time as the vehicle filled up. Once it did, a panel slid over the open top to close the cargo bay, and it sped off out the door. The conveyor belt kept running, but there was a gap of a few metres to the next plushy toy. It came closer and closer to the end - and suddenly a vehicle was driving into place, and the next creature was falling, and it just barely fell into the storage hold of the vehicle while it was driving into place.

“How scary do you find the Blight?” Alice asked.

“Scary enough that I volunteered for this mission”, Charlie said.

Alice remembered the charts they had been shown. They had been hard to miss; even the news, usually full of celebrity gossip and political machinations, had quickly switched to concentrating on the weirdness in the sky once the astronomers spotted it. Starlight dimming in many star systems and what remained of the the light spectra shifting towards the infrared. Draw a barrier around the affected area, and you get a sphere 30 light-years wide, expanding at a third of the speed of light. At the epicentre, a world that had shown all the signs of intelligent life that could be detected from hundreds of light-years away - a world that astronomers had broadcast signals to in the hopes of finally making contact with another civilisation - that had suddenly gone quiet and experienced a total loss of oxygen in its atmosphere. The Blight, they had called it.

In the following years, civilisation had mobilised. A hundred projects had sprung forth. One of them: go investigate the star system that was the second-best candidate for intelligent life, but had refused to answer radio signals, and see if someone was there to help. That was why they were here.

“I think I found something as scary as the Blight”, Alice said. “Come look at this.”

The purple creatures kept parading past the camera feed

-

Over the next five days, while the Blight advanced another forty billion kilometres towards everything they loved back home, Alice and Charlie were busy compiling a shopping catalogue.

“Computers”, Alice said. “Of every kind. A hundred varieties of phones, tablets, laptops, smartwatches, smartglasses, smart-everything.”

“Diamonds and what seems to be jewellery”, Charlie said.

“Millions of tons of every ore and mineral.” They had used their telescopes on what seemed to be a big mine, but they had barely needed them. It was like a huge gash in the flesh of a grey-fleshed and grey-blooded giant, complete with roads that looked like sutures. There were white spots in the image, tiny compared to the mine, each one a sizeable cloud.

“Clothes”, Charlie continued. “Lots and lots of clothes of different varieties. They seem to be shipped around warehouses until they’re recycled.”

“Cars. Sleek electric cars by the million. But we never see them used on the roads, though there are huge buildings were brand-new cars are recycled. And airplanes, including supersonic ones.”

“A lot of things that look like server farms”, Charlie said. “Including ones underwater and on the poles. There’s an enormous amount of compute in this world. Like, mind-boggling. I was thinking we should figure out how to plug into all of it and mine some crypt-”

“Ships with nuclear fusion reactors”, Alice interrupted. There were steady trails of them cutting shortest-path routes between points on the coast.

“Solar panels”, Charlie said. “Basically every spare surface. The building roofs are all covered with solar panels.”

“And children’s plush toys”, Alice said.

They were silent for a while.

“We have a decent idea of what these aliens looked like”, Alice said. “They were organic carbon-based lifeforms, like us. Similar in size too, also bipedal. And it’s like they left some ghostly satanic industrial amusement park running, going through all the motions in their absence, and disappeared.”

“And they didn’t go to space, as far as we know”, Charlie said.

“At least we don’t have any more Blights to worry about then”, Alice said. “I can’t help but imagining that the Blight is something like this. Something that just tiles planets with a Great Grey Grid, does something even worse to the stars, and then moves on.”

“They had space technology, but apparently whoever built the Great Grey Grid didn’t fancy it”, Charlie said. “The satellites might predate it. Probably there were satellites in lower orbits too, but their orbits decayed and they fell down, so we only see the geostationary ones up high.”

“And then what?” Alice said. “All of them vanished into thin air and left behind a highly-automated ghost-town?”

Charlie shrugged.

“Can we plug ourselves into their computers?” Alice asked.

“To mine cr-?”

“To see if anyone’s talking.”

Charlie groaned. “You can’t just plug yourself into a communication system and see anything except encrypted random-looking noise.”

“How do you know they encrypt anything?”

“It would be stupid not to”, Charlie said.

“It would be stupid to blind yourself to the rest of the universe and manufacture a billion plush toys”, Alice said.

“Seems like it will work for them until the Blight arrives.”

-

Alice floated in the middle of the central corridor of the ship. The ship was called Legacy, but even before launch they had taken to calling it “Leggy” for short. The central corridor linked the workstation at the front of the ship where they spent most of their days to the storage bay at the back. In the middle of the corridor, three doors at 120-degree angles from each other lead to the small sleeping rooms, each of them little more than a closet.

Alice had woken up only a few minutes ago, and still felt an early-morning grogginess as well as the pull of her bed. The corridor had no windows or video feeds, but was dimly lit by the artificial blue light from the workstation. They were currently on the night side of the planet.

She took a moment to look at the door of the third sleeping room. It was closed, like always, with its intended inhabitant wrapped in an air-tight seal of plastic in a closed compartment of the storage bay. They would flush him into space before they left for home again; they could have no excess mass on the ship for the return journey.

Alice thought again of the hectic preparations for the mission. Apart from Blightsource, this was only one planet the astronomers had spotted that might have intelligent life on it, and the indications were vague. But when you look into space and see something that looks like an approaching wall of death - well, that has a certain way of inspiring long-shots. Hence the mission, hence Legacy’s flight, hence crossing over the vast cold stretch of interstellar space to see if any answers could be found on this world. Hence Bob’s death while in cryonic suspension for the trip. Hence the hopes of all civilisation potentially resting on her and Charlie figuring valuable out something.

If Charlie and she could find something on this world, some piece of insight or some tool or weapon among the countless pieces of technological wizardry that this world had in spades, that had a credible chance against the Blight when it arrived … maybe there was hope.

Alice pushed off on the wall and set herself in a slow spinning motion. The ship seemed to revolve around her. Bob’s door revolved out of sight, and Charlie’s door became visible -

Wait.

Her gravity-bound instincts kicked in and she tried to stop the spin by shoving back with her hands, but there was nothing below her, so she remained spinning slowly. She breathed in deeply to calm herself down, then kicked out a foot against the wall to push herself to the opposite one. She grabbed one of the handles on the wall and held onto it.

The light on Charlie’s room was off. That meant it was empty.

“Charlie!” Alice called.

No response.

The fear came fast. Here she was, light-years from home, perhaps all alone on a spaceship tracing tight circles around a ghostly automated graveyard planet. The entire mass of the planet stood between her and the sun. Out between the stars, the Blight was closing in on her homeworld. She counted to calm herself down; one, two, three, … and just like that, the Blight was three hundred thousand kilometres closer to home. Unbidden, an image of the fluffy purple creature popped up in her mind, complete with its silly face and unblinking eye contact.

Soundlessly, she used the handles on the wall of the corridor to pull herself towards the workstation. She reached the door, peered inside -

There was Charlie, staring at a computer screen. He looked up and saw Alice. “You scared me!” he said. “Watch out, no need to sneak behind me so quietly.”

“I called your name”, Alice said.

“I know, I know”, Charlie said. “But I’m on to something here, and I just want to run a few more checks and then surprise you with the result.”

“What result?” Alice glanced at some of the screens. Two of the drones were above the Great Grey Grid, one above ocean. With their nuclear power source, they could stay in the air as long as they wanted. Even though their focus was no longer aerial reconnaissance, there was no reason not to keep them mapping the planet from up close, occasionally picking up things that their surveys from the ship did not.

“I fixed the electrical issues with the rover and the cable near the data centre”, Charlie said.

“So you’re getting data, not just frying our equipment?”

“Yes”, Charlie said. “And guess what?”

“What?”

“Guess!”

“You found a Blight-killer”, Alice said.

“No! Even better! These idiots don’t encrypt their data as far as I can tell. And I think a lot of it is natural language.”

“Okay, and can we figure out what it means?”

“We have automated programs for trying to derive syntax rules and so on”, Charlie said. “It’s already found something, including good guesses of which words are prepositions and what type of grammar they have. But mapping words to meaning based on purely statistics of how often they occur is hard.”

“I’ve seen products they have with pictures and instruction manuals”, Alice said. “We could start there.”

“Oh no”, Charlie said. “This is going to be a long process.”

-

By chance, it turned out not to be. Over the next day, they had sent a rover to a furniture factory and had managed, after some attempts, to steal an instruction leaflet out of a printer before the robotic arm could snatch it to be packaged with the furniture. Somehow Alice was reminded of her childhood adventures stealing fruit from the neighbour’s garden.

They had figured out which words meant “cupboard”, “hammer”, and “nail”, and so on. But then another rover on the other side of the world had seen something. It was exploring a grey and windy coast. On one side of the rover was the Great Grey Grid and the last road near the coast, the occasional vehicle hurtling down it. But on the other side was a stretch of rocky beach hammered by white-tipped waves, a small sliver of land that hadn’t been converted to grey.

The land rose by the beach, forming a small hill with jagged rocky sides. The sun shone down on one face of it, but there was a hollow, or perhaps small cave, that was left in the dark by the overhanging rock. And in the rock around this entrance, there were several unmistakable symbols scratched into the rock, each several metres high.

Alice took manual control of the rover and carefully instructed it to drive over the rocky beach towards the cave entrance. On the way it passed what seemed to be a fallen metal pole with some strips of fabric still clinging to it.

Once it was close enough to the mouth of what turned out to be a small cave, the camera could finally see inside.

There was a black cabinet inside. Not far from it, lying on the ground, was the skeleton of a creature with four slender limbs and a large head. Empty eye sockets stared out towards the sky.

Alice felt her heart beating fast. It wasn’t quite right; many of the anatomical details were off. But it was close enough, the similarity almost uncanny. Here, hundreds of light years away, evolution had taken a similar path, and produced sapience. And then killed it off.

“Charlie”, she said in a hoarse voice.

“What?” Charlie asked, sounding annoyed. He had been staring at an instruction manual for a chair, but he looked up and saw the video feed. “Oh”, he said, in a small voice. “We found them.”

Alice tore her eyes away from the skeleton and to the small black cabinet. It had a handle on it. She had the rover extend an arm and open it.

-

The capsule docked with Leggy and in the weightless environment they pushed the cabinet easily into the ship. They had only two there-and-back-again craft - getting back to orbit was hard - but they had quickly decided to use one to get this cabinet up. It had instructions, after all; very clear instructions, though ones that their rovers couldn’t quite follow.

It started from a pictographic representation, etched onto plastic cards, of how you were supposed to read the disks. They managed to build something that could read the microscopic grooves on the disk as per the instructions, and transfer the data to their computers.

After a few hours of work, they had figured out the encodings for numbers, the alphabet, their system of units, and seemingly also some data formats, including for images.

Confirmation came next. The next item on the disk was an image of two of the living aliens, standing on a beach during a sunset. Alice stared into their faces for a long time.

Next there came images next to what were clearly words of text, about fifty of them. Some of the more abstract ones took a few guesses, but ultimately they thought they had a base vocabulary, and with the help of some linguistics software, it did not take very long before they had a translated vocabulary list of about eight thousand words.

Alice was checking the work when Charlie almost shouted: “Look at this!”

Alice looked at what he was pointing at. It was a fragment of text that read:

Hello,

The forms for ordering the new furniture are attached. Please fill them in and we will respond to your order as quickly as we can!

If you need any help, please contact customer support. You will find the phone number on our website.

“What is this? Is Mr Skeleton trying to sell us furniture from beyond the grave?” Alice asked.

“No”, Charlie said. “This isn’t what I got from the recovered data; I haven’t looked at the big remaining chunk yet. This is what I got by interpreting one of the packets of data running on the cables that our rover is plugged into using what we now know about their data formats and the language.”

“And?”

“I don’t get it!” Charlie said. “Why would a world of machines send each other emails in natural language?”

“Why would they manufacture plushy toys? I doubt the robotic arms need cuddles.”

Charlie looked at the world, slowly spinning underneath their ship. “Being so close to it makes me feel creeped out. I don’t get it.”

“You don’t want to lick it anymore?” Alice asked. She decided not to tell Charlie about her own very similar feelings earlier, when she thought for a moment Charlie had gone missing.

Charlie ignored her. “I think the last thing on Mr Skeleton’s hard-drive is a video”, he said. “I’ve checked and it seems to play.”

“You looked at it first?” Alice said in a playfully mocking tone. The thrill of discovery was getting to her.

“Only the first five frames”, Charlie said. “Do you want to watch it?”

-

Our Civilisation: A Story read a short fragment of subtitle, white on black, auto-translated by a program using the dictionary they had built up.

There was a brief shot of some semi-bipedal furry creature walking in the forest. Then one of a fossilised skeleton of something more bipedal and with a bigger head. Then stone tools: triangular ones that might have been spear tips, saw-toothed ones, clubs. A dash of fading red paint on a rock surface, in the shape of a cartoon version of that same bipedal body plan.

There were two pillars of stone in a desert on what looked like a pedestal, some faded inscription at its base and the lone and level sands stretching far away. There was a shot of an arrangement of rocks, some balancing on top of two others, amid a field of green. A massive pyramidal stone structure, lit by the rising sun.

Blocky written script etched on a stone tablet. Buildings framed by columns of marble. A marble statue of one of the aliens, a sling carelessly slung over its shoulder, immaculate in its detail. A spinning arrangement of supported balls orbiting a larger one. And still it moves, the subtitles flashed.

A collection of labelled geometric diagrams on faded yellow paper. Mathematical Principles of Natural Philosophy.

A great ornate building with a spire. A painting of a group of the aliens clad in colourful clothing. An ornate piece of writing. We hold these truths to be self-evident …

A painting of a steam locomotive barrelling along tracks. A diagram of a machine. A black-and-white picture of one of the aliens, then another. Government of the people, for the people, by the people, shall not perish …

An alien with white hair sticking up, holding a small stick of something white and with diagrams of cones behind him. Grainy footage of propeller aircraft streaking through the sky, and then of huge masses of people huddling together and walking across a barren landscape, and then of aliens all in the same clothes charging a field, some of them suddenly jerking about and falling to the ground. We will fight on the beaches, we will fight on the landing grounds …

A black-and-white footage of a mushroom cloud slowly rising from a city below. A picture, in flat pale blue and white, showing a stylised representation of the world’s continents. The same picture, this time black-and-white, on the wall of a room where at least a hundred aliens were sitting.

An alien giving a speech. I have a dream. An alien, looking chubby in a space suit, standing on a barren rocky surface below an ink-black sky next to a pole with a colourful rectangle attached to it.

Three aliens in a room, looking at the camera and holding up a piece of printed text. Disease eradicated.

What looked like a primitive computer. A laptop computer. An abstract helical structure of balls connected by rods, and then flickering letters dancing across the screen.

A blank screen, an arrow extending left to right across it - time, flashed the subtitles- and then another arrow from the bottom-left corner upwards - people in poverty - and then a line crawling from left to right, falling as it did so.

A line folding itself up into a complicated shape. AI system cracks unsolved biology problem.

From then on, the screen showed pictures of headlines.

All routine writing tasks now a solved problem, claims AI company.

Office jobs increasingly automated.

Three-fourths of chief executives of companies on the [no translation] admit to using AI to help write emails, one-third have had AI write a shareholder letter or strategy document.

Exclusive report: world’s first fully-automated company, a website design agency.

Mass layoffs as latest version of [no translation] adopted at [no translation]; ‘stunning performance’ at office work.

Nations race to reap AI productivity gains: who will gain and who will lose?

CEO of [no translation] resigns, claiming job pointless, both internal and board pressure to defer to “excellently-performing” AI in all decisions.

[No translation] ousts executive and management team, announces layoffs; board supports replacing them with AI to keep up with competition.

Entirely or mostly automated companies now delivering 2.5x higher returns on investment on average; ‘the efficiency difference is no joke’, says chair of [no translation].

Year-on-year economic growth hits 21% among countries with advanced AI access.

Opinion: the new automated economy looks great on paper but is not serving the needs of real humans.

Mass protests after [no translation], a think-tank with the ear of the President, is discovered to be funded and powered by AI board of [no translation], and to have practically written national economic policy for the past two years.

‘No choice but forward’, says [no translation] after latest round of worries about AI; unprecedented economic growth still strong.

[No translation 1] orders raid of [no translation 2] over fears [no translation 2] is not complying with latest AI use regulations, but cannot execute order due to noncompliance from the largely-automated police force; ‘we are working with our AI advisers and drivers in accordance with protocol, and wish to assure the [no translation 3] people that we are still far from the sci-fi scenario where our own police cars have rebelled against us.’

‘AI overthrow’ fears over-hyped, states joint panel of 30 top AI scientists and business-people along with leading AI advisory systems; ‘they’re doing a good job maximising all relevant metrics and we should let them keep at it, though businesses need to do a better job of selecting metrics and tough regulation is in order.’

Opinion: we’re better-off under a regime of rigorous AI decision-making than under corrupt politicians; let the AIs repeat in politics what they’ve done for business over the last five years.

‘The statistics have never looked so good’ - Prime Minister reassures populace as worries mount over radical construction projects initiated by top AI-powered companies.

Expert panel opinion: direct AI overthrow scenario remains distant threat, but more care should be exercised over choice of target metrics; recommend banning of profit-maximisation target metric.

Movement to ban profit-maximising AIs picks up pace.

Top companies successfully challenge new AI regulation package in court.

‘The sliver of the economy over which we retain direct control will soon be vanishingly small’, warns top economist, ‘action on AI regulation may already be too late’.

Unverified reports of mass starvation in [no translation]; experts blame agricultural companies pivoting to more land-efficient industries.

Rant goes viral: ‘It’s crazy, man, we just have these office AIs that only exist in the cloud, writing these creepily-human emails to other office AIs, all overseen by yet another AI, and like most of their business is with other AI companies; they only talk to each other, they buy and sell from each other, they do anything as long as it makes those damned numbers on their spreadsheets just keep ticking up and up; I don’t think literally any human has ever seen a single product out of the factory that just replaced our former neighbourhood, but those factories just keep going up everywhere.’

Revolution breaks out in [no translation]; government overthrown, but it’s business-as-usual for most companies, as automated trains, trucks, and ships keep running.

[No translation] Revolution: Leaked AI-written email discovered, in which the AI CEO ordered reinforcement of train lines and trains three weeks ago. ‘We are only trying to ensure the continued functioning of our supply chains despite the recent global unrest, in order to best serve our customers’, CEO writes in new blog post.

[No translation] Revolution: crowds that tried swarming train lines run over by trains; ‘the trains didn’t even slow down’, claim witnesses. CEO cites fiduciary duties.

Despite unprecedented levels of wealth and stability, you can’t actually do much: new report finds people trying to move house, book flight or train tickets, or start a new job or company often find it difficult or impossible; companies prioritising serving ‘more lucrative’ AI customers and often shutting down human-facing services.

Expert report: ‘no sign of human-like consciousness even in the most advanced AI systems’, but ‘abundantly clear’ that ‘the future belongs to them’.

New report: world population shrinking rapidly; food shortages, low birth rates, anti-natalist attitudes fuelled by corporate campaigns to blame.

The screen went blank. Then a video of an alien appeared, sitting up on a rocky surface. Alice took a moment to realise that it’s the same cave they found the skeleton in. The alien’s skin was wrapped tight around its bones, and even across the vast gulf of biology and evolutionary history, Alice could tell that it is not far from death. It opened its mouth, and sound came out. Captions appeared beneath it.

“It is the end”, the alien said, its eyes staring at them from between long unkempt clumps of hair. “On paper, I am rich beyond all imagination. But I have no say in this new world. And I cannot find food. I will die.”

The wind tugged at the alien’s long hair, but otherwise the alien was so still that Alice wondered if it had died there and then.

“There is much I would like to say”, the alien says. “But I do not have the words, and I do not have the energy.” It paused. “I hope it was not all in vain. Or, that if for us it was, that for someone up there it isn’t.”

The video went blank.

Alice and Charlie watched the blank screen in silence.

“At least the blight they birthed seems to have stuck to their world”, Charlie said after a while.

“Yeah”, Alice said, slowly. “But I don’t think we’ll find anything here.”

Legacy completed nine more orbits of the planet, and then jettisoned all unnecessary mass into space. Its engines jabbed against the darkness of space, bright enough to be visible from the planet’s surface. There was no one to see them.

On a factory down on the planet, an assembly line of beady-eyed purple plush toys marched on endlessly.


The title of this work is taken from a passage in Superintelligence: Paths, Dangers, Strategies, where Nick Bostrom writes:

We could thus imagine, as an extreme case, a technologically highly advanced society, containing many complex structures, some of them far more intricate and intelligent than anything that exists on the planet today—a society which nevertheless lacks any type of being that is conscious or whose welfare has moral significance. In a sense, this would be an uninhabited society. It would be a society of economic miracles and technological awesomeness, with nobody there to benefit. A Disneyland without children. [emphasis added]

The outline of events presented draws inspiration from several sources, but most strongly on Paul Christiano’s article What failure looks like.