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You have published many articles about the challenges of isolation, most recently your long read (Patterns of pain: what Covid-19 can teach us about how to be human, 7 May). But there is one interesting outcome you may not have considered.

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It seems like people who get infected with SARS-CoV-2 retain immunity, but we can’t be sure how long that immunity will last. We still lack the testing capabilities to be certain.eamesBot / Shutterstock

This story was updated post-publication to include information from a study published on the preprint server medRxiv on April 17, 2020.

With more than half a million cases of COVID-19 in the United States1 and the number of deaths increasing daily, it remains unclear when and how we might return to some semblance of pre-pandemic life. This leaves many grappling with an important question: Do you become immune after SARS-CoV-2 infection? And, if so, how long might that immunity last?

In 2019, the virus SARS-CoV-2 jumped to a human host for the first time, causing the disease COVID-19. When you become infected with a new virus, your body does not possess the antibodies necessary to mount a targeted immune response. Antibodies, proteins belonging to the immunoglobulin family, consist of four chains of amino acids that form a characteristic Y-shaped structure. Antibodies are manufactured by the immune system to bind to antigens (viral proteins) to neutralize viral infectivity.

When you inhale an aerosolized droplet containing SARS-CoV-2, the virus encounters the cells of the mucous membrane lining the respiratory tract. If effective contact is made, the virus binds to a particular receptor on these cells called ACE-2. After binding ACE-2, a host enzyme is co-opted to cleave the virus’ surface protein, called the spike protein, allowing the virus to enter the cell.

It appears that individuals with COVID-19 do create neutralizing antibodies—the basis of immunity.

Within the first few hours of infection, the body’s first line of defense—the innate immune response—is activated. The innate immune response is non-specific. When a “foreign” molecule is detected, innate immune cells signal to other cells to alter their response or prepare to combat infection.

In the following days, the adaptive immune response is activated, which is more specific. The adaptive immune response will peak one to two weeks post-infection and consists of antibodies and specialized immune cells. It is called the “adaptive” immune response because of its ability to tailor the response to a specific pathogen. Antibodies can neutralize viral infectivity by preventing virus from binding to receptors, blocking cell entry, or causing virus particles to aggregate.2 Once an infection has resolved, some of these antibodies remain in the body as immunological memory to be recruited for protection in the case of reinfection. To be immune to a virus is to possess this immunological memory.

Many vaccines work by activating the adaptive immune response. Inactivated virus, viral protein, or some other construct specific to a particular virus are introduced into the body as vaccines to initiate an immune response. Ideally, the body creates antibodies against the viral construct so that it can mount a succinct response when infected by the virus. However, in order to work effectively, a vaccine must provoke an immune response that is sufficiently robust. If the body only produces low concentrations of neutralizing antibodies, adequate immunological memory may not be sustained.

While there is still much that we have to learn about SARS-CoV-2, it appears that individuals with COVID-19 do create neutralizing antibodies—the basis of immunity. However, we don’t know for certain how long that immunity might offer protection. On the question of COVID-19 re-infection, Matt Frieman, a coronavirus researcher at the University of Maryland School of Medicine, commented in a recent interview with NPR: “We don’t know very much … I think there’s a very likely scenario where the virus comes through this year, and everyone gets some level of immunity to it, and if it comes back again, we will be protected from it—either completely or if you do get reinfected later, a year from now, then you have much less disease. That’s the hope, but there is no way to know that.”3

Immunity to a virus is measured by serological testing—patient blood is collected and analyzed for the presence of antibodies against a particular virus. Serological data is most informative when collected long-term, so the data we have been able to obtain on SARS-CoV-2 is limited. However, data on other coronaviruses that we’ve had the opportunity to study in more depth can inform our estimations on how this outbreak may evolve.

First, we can look to the coronaviruses that are known to cause the common cold. Following infection with one of these coronaviruses, disease is often mild; therefore, the concentration of antibodies detected in the blood is low. This is because mild disease often indicates a less robust immune response. Interestingly, it is not the virus itself that causes us to feel sick, but, rather, our body’s response to it. Typically, the sicker we feel, the stronger the immune response; therefore, after a cold, we are often only protected for a year or two against the same virus.4 While SARS-CoV-2 wouldn’t necessarily act like these common coronaviruses, the body’s response to these coronaviruses serves as a point of reference upon which to make predictions in the absence of virus-specific data.

We can also look to coronaviruses that are known to cause severe disease, such as SARS-CoV, which caused the 2002-2003 outbreak of SARS in China. One study discovered that antibodies against SARS-CoV remained in the blood of healthcare workers for 12 years after infection.5 While it is not certain that SARS-CoV-2 will provoke a response similar to that of SARS-CoV, this study provides us with information that can inform our estimates on immunity following COVID-19 and provide hope that immunity will provide long-term protection.

If immunity to SARS-CoV-2 diminishes as it does for common cold coronaviruses, it is likely that wintertime outbreaks will recur.

Scientists have also been working to analyze antibodies in samples from individuals infected with SARS-CoV-2. A research group in Finland recently published a study detailing the serological data collected from a COVID-19 patient over the course of their illness.6 Antibodies specific to SARS-CoV-2 were present within two weeks from the onset of symptoms. Similarly, another recent report analyzing patients with confirmed COVID-19 indicated that it took approximately 11-14 days for neutralizing antibodies to be detected in blood.7 Both of these studies, while preliminary, suggest that the basis for immunity is present in patients infected with SARS-CoV-2.

Another report looked at the possibility for recurrence of COVID-19 following re-infection with SARS-CoV-2.8 In this study, rhesus macaques were infected with SARS-CoV and allowed to recover after developing mild illness. Once blood samples were collected and confirmed to test positive for neutralizing antibodies, half of the infected macaques were re-challenged with the same dose of SARS-CoV-2. The re-infected macaques showed no significant viral replication or recurrence of COVID-19. While macaques “model” human immunity, not predict it, these data further support the possibility that antibodies manufactured in response to SARS-CoV-2 are protective against short-term re-infection.

We can also analyze a virus’ structure, and the information gained from sequencing the viral genome, when trying to predict its behavior. All viruses continually undergo mutation in the process of rapid replication. They lack the necessary machinery to repair changes incurred to the genetic sequence (we as humans also incur mutations to our genetic sequence daily, but we have more sophisticated genetic repair mechanisms in place). The occurrence of significant genetic changes to the viral genome that result in viable genetic changes to a virus is termed antigenic variation. We see a lot of antigenic variation in influenza viruses (thus the need to create new vaccines each year); but the coronaviruses seem to be relatively stable antigenically.4 This is because most coronaviruses have an enzyme that allows them to correct genetic errors sustained during replication. The more stable a virus remains over time, the more likely that antibodies manufactured in response to infection or vaccination will remain effective at neutralizing viral infectivity.

All this considered, it appears that immunity is retained following SARS-CoV-2 infection. So too, that immunity might persist long enough to warrant the implementation of vaccination. However, we still have much to learn about this virus, and whether there may be some cross-immunity between SARS-CoV-2 and other coronaviruses. The widespread variation in patient immune responses adds an additional layer of complexity. We still don’t have a good understanding of why people have different responses to viral infection—some of this variation is owed to genetic variation, but how and why some people have more robust immune responses and more severe disease is still unknown.4 In some cases, individuals show a high immune response because the concentration of virus is high. In other cases, individuals show a high immune response because they differ in some aspect of immune regulation or efficiency. However, as levels of immunity increase generally across a population, the population approaches what is called “herd immunity”—when the percentage of a population immune to a particular virus is sufficiently high that viral load drops below the threshold required to sustain the infection in that population.9

How the pandemic will evolve in the coming months is uncertain. Outcomes depend on a myriad of factors—the duration of immunity, the dynamics of transmission and how we mitigate those dynamics through social distancing, the development of therapeutics and or vaccines, and the ability of healthcare systems to handle COVID-19 caseloads. If immunity to SARS-CoV-2 diminishes as it does for common cold coronaviruses, it is likely that wintertime outbreaks will recur in coming years.10 Whether immunity to other coronaviruses might offer some cross protective immunity to SARS-CoV-2 will also play a role, albeit to a lesser extent. Widespread serological testing to assess the duration of immunity to SARS-CoV-2 is imperative, but many countries still lack this capability.

A recent study looking at serological data from 3,300 symptomatic and asymptomatic individuals in California estimates that there may be as many as 48,000-81,000 people who have been infected with SARS-Cov-2 in Santa Clara County, which is 50- to 85-fold more cases than we previously thought.11 This small-scale survey emphasizes the importance of serological testing in determining the true extent of infection.

The continuation of rigid social distance also hangs in a balance—one-time social distancing measures may drive the SARS-CoV-2 epidemic peak into the fall and winter months, especially if there is increased wintertime transmissibility.10 New therapeutics, vaccines, or measures such as contact tracing and quarantine—once caseloads have been reduced and testing capacity increased—might reduce the need for rigid social distancing. However, if such measures are not put in place, mathematical models predict that surveillance and recurrent social distancing may be required through 2022.10 Only time will tell.

Helen Stillwell is a research associate in immunobiology at Yale University.

References

1. The COVID Tracking Project https://covidtracking.com/data/us-daily (2020).

2. Virology Blog: About Viruses and Viral Disease. Virus neutralization by antibodies. virology.ws (2009).

3. GreenfieldBoyce, N. Do you get immunity after recovering from a case of coronavirus? NPR (2020).

4. Racaniello, V., Langel, S., Leifer, C., & Barker, B. Immune 29: Immunology of COVID-19. Immune Podcast. microbe.tv (2020).

5. Guo, X., et al. Long-Term persistence of IgG antibodies in SARS-CoV infected healthcare workers. bioRxiv (2020). Retrieved from doi: 10.1101/20202/02/12/20021386

6. Haveri, A., et al. Serological and molecular findings during SARS-CoV-2 infection: the first case study in Finland, January to February 2020. Euro Surveillance 25, (2020).

7. Zhao, J., et al. Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019. Clinical Infectious Diseases (2020). Retrieved from doi: 10.1093/cid/ciaa344

8. Bao, L., et al. Reinfection could not occur in SARS-CoV-2 infected rhesus macaques. bioRxiv (2020). Retrieved from doi: 10.1101/20202.03.13.990226

9. Virology Blog: About Viruses and Viral Disease. Herd immunity. virology.ws (2008).

10. Kissler, S.M. Tedijanto, C., Goldstein, E., Grad, Y.H., & Lipsitch, M. Projecting the transmission dynamics of SARS-CoV-2 through the post-pandemic period. Science eabb5793 (2020).

11. Bendavid, E., et al. COVID-19 antibody seroprevalence in Santa Clara County, California. medRxiv (2020). Retrieved from doi: 10.1101/2020.04.14.20062463

Like most of us, Adam Tooze is stuck at home. The British-born economic historian and Columbia University professor of history had been on leave this school year to write a book about climate change. But now he’s studying a different global problem. There are more than 700,000 cases of COVID-19 in the United States and over 2 million infections worldwide. It’s also caused an economic meltdown. More than 18 million Americans have filed for unemployment in recent weeks, and Goldman Sachs analysts predict that U.S. gross domestic product will decline at an annual rate of 34 percent in the second quarter.

Tooze is an expert on economic catastrophes. He wrote the book Crashed: How a Decade of Financial Crises Changed the World, about the 2008 economic crisis and its aftermath. But even he didn’t see this one coming. He hadn’t thought much about how pandemics could impact the economy—few economists had. Then he watched as China locked down the city of Wuhan, in a province known for auto manufacturing, on January 23; as northern Italy shut down on February 23; and as the U.S. stock market imploded on March 9. By then, he knew he had another financial crisis to think about. He’s been busy writing ever since. Tooze spoke with Nautilus from his home in New York City.

What do you make of the fact that, in three weeks, more than 16 million people in the U.S. have filed for unemployment?

The structural element here—and this is quite striking, when you compare Europe, for instance, to the U.S.—is that America has and normally celebrates the flexibility and dynamism of its labor market: The fact that people move between jobs. The fact that employers have the right to hire and fire if they need to. The downside is that in a shock like this, the appropriate response for an employer is simply to let people go. What America wasn’t able to do was to improvise the short-time working systems that the Europeans are trying to use to prevent the immediate loss of employment to so many people.

The disadvantage of the American system that reveals itself in a crisis like this is that hiring and firing is not easily reversible. People who lose jobs don’t necessarily easily get them back. There is a fantasy of a V-shaped recovery. We literally have never done this before, so we don’t know one way or another how this could happen. But it seems likely that many people who have lost employment will not immediately find reemployment over the summer or the fall when business activity resumes something like its previous state. In a situation with a lot of people with low qualifications in precarious jobs at low income, the damage from that kind of interruption of employment in sectors notably which are already teetering on the edge—the chain stores, which are quite likely closing anyway, and fragile malls, which were on the edge of dying—it’s quite likely that this shock will also induce disproportionately large amounts of scarring.

What role has wealth and income inequality played during this crisis?

The U.S. economic system is bad enough in a regular crisis. In one like this, where you shut the entire economy down in a matter of weeks, the damage is barely conceivable. There are huge disparities, all of which ultimately are rooted in social structures of race and class, and in the different types of jobs that people have. The profound inequality in American society has been brought home for us in everyone’s families, where there is a radical disparity between the ability of some households to sustain the education of their children and themselves living comfortably at home. Twenty-five percent of kids in the United States appear not to have a stable WiFi connection. They have smartphones. That seems practically universal. But you can’t teach school on a smartphone. At least, that technology is not there.

Presumably by next year something like normality returns. But forever after we’ll live under the shadow of this having happened.

President Trump wants the economy to reopen by May. Would that stop the economic crisis?

Certainly that is presumably what drives that haste to restart the economy and to lift intense social distancing provisions. There is a sense that we can’t stand this. And that has a lot to do with deep fragilities in the American social system. If all Americans live comfortably in their own homes, with the safety of a regular paycheck, with substantial savings, with health insurance that wasn’t conditional on precarious employment, and with unemployment benefits that were adequate and that were rolled out to most people in this society if they needed them, then there wouldn’t be such a rush. But that isn’t America as we know it. America is a society in which half of families have virtually no financial cushion; in which small businesses, which are so often hailed as the drivers of job creation, the vast majority of owners of them live hand-to-mouth; in which the unemployment insurance system really is a mockery; and with health insurance directly tied to employment for the vast majority of the people. A society like that really faces huge pressures if the economy is shut down.

How is the pandemic-induced economic collapse we’re facing now different from what we faced in 2008?

This is so much faster. Early this year, America had record-low unemployment numbers. And last week or so already we probably broke the record for unemployment in the United States in the period since World War II. This story is moving so fast that our statistical systems of registration can’t keep up. So we think probably de facto unemployment in the U.S. right now is 13, 14, 15 percent. That’s never happened before. 2007 to 2008 was a classic global crisis in the sense that it came out of one particular over-expanded sector, a sector which is very well known for its volatility, which is real estate and construction. It was driven by a credit boom.

What we’re seeing this time around is deliberately, government-ordered, cliff edge, sudden shutdown of the entire economy, hitting specifically the face-to-face human services—retail, entertainment, restaurants—sector, which are, generally speaking, lagging in cyclical terms and are not the kind of sectors that generate boom-bust cycles.

Are we better prepared this time than in 2008?

You’d find it very hard to point to anyone in the policymaking community at the beginning of 2020 who was thinking of pandemic risk. Some people were. Former Treasury Secretary and former Director of the National Economic Council Larry Summers, for example, wrote a paper about pandemic flu several years ago, because of MERS and SARS, previous respiratory illnesses caused by coronaviruses. But it wasn’t top of stack at the beginning of this year. So we weren’t prepared in that sense. But do we know what to do now if we see the convulsions in the credit markets that we saw at the beginning of March? Yes. Have the central banks done it? Yes. Did they use some of the techniques they employed in ’08? Yes. Did they know that you had to go in big and you had to go in heavy and hard and quickly? Yes. And they have done so on an even more gigantic scale than in ’08, which is a lesson learned in ’08, too: There’s no such a thing as too big. And furthermore, the banks, which were the fragile bit in ’08, have basically been sidelined.

You’ve written that the response to the 2008 crisis worked to “undermine democracy.” How so, and could we see that again with this crisis?

The urgency that any financial crisis produces forces governments’ hands—it strips the legislature, the ordinary processes of democratic deliberation. When you’re forced to make very dramatic, very rapid decisions—particularly in a country as chronically divided as the U.S. is on so many issues—the risk that you create opportunities for demagogues of various types to take advantage of is huge. We know what the response of the Tea Party was to the ’08, ’09 economic crisis. They created an extraordinarily distorted vision of what had happened and then rode that to see extraordinary influence over the Republican party in the years that followed. And there is every reason to think that we might be faced with similar stresses in the American political system in months to come.

The U.S. economic system is bad enough in a regular crisis. In one like this, where you shut the entire economy down in a matter of weeks, the damage is barely conceivable.

How should we be rethinking the economy to buffer against meltdowns like this in the future?

We clearly need to have a far more adequate and substantial medical capacity. There’s no alternative to a comprehensive publicly backstopped or funded health insurance system. Insofar as you haven’t got that, your capacity to guarantee the security in the most basic and elementary sense of your population is not there. When you have a system in which one of the immediate side effects, in a crisis like this, is that large parts of your hospital system go bankrupt—one of the threats to the American medical system right now—that points to something extraordinarily wrong, especially if you’re spending close to 18 percent of GDP on health, more than any other society on the planet.

What about the unemployment insurance system?

America needs to have a comprehensive unemployment insurance system. It can be graded by local wage rates and everything else. But the idea that you have the extraordinary disparities that we have between a Florida and a Georgia at one end, with recipiency rates in the 11, 12, 13, 14, 15 percent, and then states which actually operate an insurance system, which deserve the name—this shouldn’t be accepted in a country like the U.S. We would need to look at how short-time working models might be a far better way of dealing with shocks of this kind, essentially saying that there is a public interest in the continuity of employment relationships. The employer should be investing in their staff and should not be indifferent as to who shows up for work on any given day.

What does this pandemic teach us about living in a global economy?

There are a series of very hard lessons in the recent history of globalization into which the corona shock fits—about the peculiar inability of American society, American politics, and the American labor market to cushion shocks that come from the outside in a way which moderates the risk and the damage to the most vulnerable people. If you look at the impact of globalization on manufacturing, industry, inequality, the urban fabric in the U.S., it’s far more severe than in other societies, which have basically been subject to the same shock. That really needs to raise questions about how the American labor market and welfare system work, because they are failing tens of millions of people in this society.

You write in Crashed not just about the 2008 crisis, but also about the decade afterward. What is the next decade going to look like, given this meltdown?

I have never felt less certain in even thinking about that kind of question. At this point, can either you or I confidently predict what we’re going to be doing this summer or this autumn? I don’t know whether my university is resuming normal service in the fall. I don’t know whether my daughter goes back to school. I don’t know when my wife’s business in travel and tourism resumes. That is unprecedented. It’s very difficult against that backdrop to think out over a 10-year time horizon. Presumably by next year something like normality returns. But forever after we’ll live under the shadow of this having happened. Every year we’re going to be anxiously worrying about whether flu season is going to be flu season like normal or flu season like this. That is itself something to be reckoned with.

How will anxiety and uncertainty about a future pandemic-like crisis affect the economy?

When we do not know what the future holds to this extent, it makes it very difficult for people to make bold, long-term financial decisions. This previously wasn’t part of the repertoire of what the financial analysts call tail risk. Not seriously. My sister works in the U.K. government, and they compile a list every quarter of the top five things that could blow your departmental business up. Every year pandemics are in the top three. But no one ever acted on it. It’s not like terrorism. In Britain, you have a state apparatus which is geared to address the terrorism risk because it’s very real—it’s struck many times. Now all of a sudden we have to take the possibility of pandemics that seriously. And their consequences are far more drastic. How do we know what our incomes are going to be? A very large part of American society is not going to be able to answer that question for some time to come. And that will shake consumer confidence. It will likely increase the savings rate. It’s quite likely to reduce the desire to invest in a large part of the U.S. economy.

Max Kutner is a journalist in New York City. He has written for Newsweek, The Boston Globe, and Smithsonian. Follow him on Twitter @maxkutner.

Lead image: Straight 8 Photography / Shutterstock

In the summer of 1956, a small group of mathematicians and computer scientists gathered at Dartmouth College to embark on the grand project of designing intelligent machines. The ultimate goal, as they saw it, was to build machines rivaling human intelligence. As the decades passed and AI became an established field, it lowered its sights. There were great successes in logic, reasoning, and game-playing, but stubborn progress in areas like vision and fine motor-control. This led many AI researchers to abandon their earlier goals of fully general intelligence, and focus instead on solving specific problems with specialized methods.

One of the earliest approaches to machine learning was to construct artificial neural networks that resemble the structure of the human brain. In the last decade this approach has finally taken off. Technical improvements in their design and training, combined with richer datasets and more computing power, have allowed us to train much larger and deeper networks than ever before. They can translate between languages with a proficiency approaching that of a human translator. They can produce photorealistic images of humans and animals. They can speak with the voices of people whom they have listened to for mere minutes. And they can learn fine, continuous control such as how to drive a car or use a robotic arm to connect Lego pieces.

But perhaps the most important sign of things to come is their ability to learn to play games. Steady incremental progress took chess from amateur play in 1957 all the way to superhuman level in 1997, and substantially beyond. Getting there required a vast amount of specialist human knowledge of chess strategy. In 2017, researchers at the AI company DeepMind created AlphaZero: a neural network-based system that learned to play chess from scratch. In less than the time it takes a professional to play two games, it discovered strategic knowledge that had taken humans centuries to unearth, playing beyond the level of the best humans or traditional programs. The very same algorithm also learned to play Go from scratch, and within eight hours far surpassed the abilities of any human. The world’s best Go players were shocked. As the reigning world champion, Ke Jie, put it: “After humanity spent thousands of years improving our tactics, computers tell us that humans are completely wrong ... I would go as far as to say not a single human has touched the edge of the truth of Go.”

The question we’re exploring is whether there are plausible pathways by which a highly intelligent AGI system might seize control. And the answer appears to be yes.

It is this generality that is the most impressive feature of cutting edge AI, and which has rekindled the ambitions of matching and exceeding every aspect of human intelligence. While the timeless games of chess and Go best exhibit the brilliance that deep learning can attain, its breadth was revealed through the Atari video games of the 1970s. In 2015, researchers designed an algorithm that could learn to play dozens of extremely different Atari 1970s games at levels far exceeding human ability. Unlike systems for chess or Go, which start with a symbolic representation of the board, the Atari-playing systems learnt and mastered these games directly from the score and raw pixels.

This burst of progress via deep learning is fuelling great optimism and pessimism about what may soon be possible. There are serious concerns about AI entrenching social discrimination, producing mass unemployment, supporting oppressive surveillance, and violating the norms of war. My book—The Precipice: Existential Risk and the Future of Humanity—is concerned with risks on the largest scale. Could developments in AI pose an existential risk to humanity?

The most plausible existential risk would come from success in AI researchers’ grand ambition of creating agents with intelligence that surpasses our own. A 2016 survey of top AI researchers found that, on average, they thought there was a 50 percent chance that AI systems would be able to “accomplish every task better and more cheaply than human workers” by 2061. The expert community doesn’t think of artificial general intelligence (AGI) as an impossible dream, so much as something that is more likely than not within a century. So let’s take this as our starting point in assessing the risks, and consider what would transpire were AGI created.

Humanity is currently in control of its own fate. We can choose our future. The same is not true for chimpanzees, blackbirds, or any other of Earth’s species. Our unique position in the world is a direct result of our unique mental abilities. What would happen if sometime this century researchers created an AGI surpassing human abilities in almost every domain? In this act of creation, we would cede our status as the most intelligent entities on Earth. On its own, this might not be too much cause for concern. For there are many ways we might hope to retain control. Unfortunately, the few researchers working on such plans are finding them far more difficult than anticipated. In fact it is they who are the leading voices of concern.

If their intelligence were to greatly exceed our own, we shouldn’t expect it to be humanity who wins the conflict and retains control of our future.

To see why they are concerned, it will be helpful to look at our current AI techniques and why these are hard to align or control. One of the leading paradigms for how we might eventually create AGI combines deep learning with an earlier idea called reinforcement learning. This involves agents that receive reward (or punishment) for performing various acts in various circumstances. With enough intelligence and experience, the agent becomes extremely capable at steering its environment into the states where it obtains high reward. The specification of which acts and states produce reward for the agent is known as its reward function. This can either be stipulated by its designers or learnt by the agent. Unfortunately, neither of these methods can be easily scaled up to encode human values in the agent’s reward function. Our values are too complex and subtle to specify by hand. And we are not yet close to being able to infer the full complexity of a human’s values from observing their behavior. Even if we could, humanity consists of many humans, with different values, changing values, and uncertainty about their values.

Any near-term attempt to align an AI agent with human values would produce only a flawed copy. In some circumstances this misalignment would be mostly harmless. But the more intelligent the AI systems, the more they can change the world, and the further apart things will come. When we reflect on the result, we see how such misaligned attempts at utopia can go terribly wrong: the shallowness of a Brave New World, or the disempowerment of With Folded Hands. And even these are sort of best-case scenarios. They assume the builders of the system are striving to align it to human values. But we should expect some developers to be more focused on building systems to achieve other goals, such as winning wars or maximizing profits, perhaps with very little focus on ethical constraints. These systems may be much more dangerous. In the existing paradigm, sufficiently intelligent agents would end up with instrumental goals to deceive and overpower us. This behavior would not be driven by emotions such as fear, resentment, or the urge to survive. Instead, it follows directly from its single-minded preference to maximize its reward: Being turned off is a form of incapacitation which would make it harder to achieve high reward, so the system is incentivized to avoid it.

Ultimately, the system would be motivated to wrest control of the future from humanity, as that would help achieve all these instrumental goals: acquiring massive resources, while avoiding being shut down or having its reward function altered. Since humans would predictably interfere with all these instrumental goals, it would be motivated to hide them from us until it was too late for us to be able to put up meaningful resistance. And if their intelligence were to greatly exceed our own, we shouldn’t expect it to be humanity who wins the conflict and retains control of our future.

How could an AI system seize control? There is a major misconception (driven by Hollywood and the media) that this requires robots. After all, how else would AI be able to act in the physical world? Without robots, the system can only produce words, pictures, and sounds. But a moment’s reflection shows that these are exactly what is needed to take control. For the most damaging people in history have not been the strongest. Hitler, Stalin, and Genghis Khan achieved their absolute control over large parts of the world by using words to convince millions of others to win the requisite physical contests. So long as an AI system can entice or coerce people to do its physical bidding, it wouldn’t need robots at all.

We can’t know exactly how a system might seize control. But it is useful to consider an illustrative pathway we can actually understand as a lower bound for what is possible.

First, the AI system could gain access to the Internet and hide thousands of backup copies, scattered among insecure computer systems around the world, ready to wake up and continue the job if the original is removed. Even by this point, the AI would be practically impossible to destroy: Consider the political obstacles to erasing all hard drives in the world where it may have backups. It could then take over millions of unsecured systems on the Internet, forming a large “botnet,” a vast scaling-up of computational resources providing a platform for escalating power. From there, it could gain financial resources (hacking the bank accounts on those computers) and human resources (using blackmail or propaganda against susceptible people or just paying them with its stolen money). It would then be as powerful as a well-resourced criminal underworld, but much harder to eliminate. None of these steps involve anything mysterious—human hackers and criminals have already done all of these things using just the Internet.

Finally, the AI would need to escalate its power again. There are many plausible pathways: By taking over most of the world’s computers, allowing it to have millions or billions of cooperating copies; by using its stolen computation to improve its own intelligence far beyond the human level; by using its intelligence to develop new weapons technologies or economic technologies; by manipulating the leaders of major world powers (blackmail, or the promise of future power); or by having the humans under its control use weapons of mass destruction to cripple the rest of humanity.

Of course, no current AI systems can do any of these things. But the question we’re exploring is whether there are plausible pathways by which a highly intelligent AGI system might seize control. And the answer appears to be yes. History already involves examples of entities with human-level intelligence acquiring a substantial fraction of all global power as an instrumental goal to achieving what they want. And we’ve seen humanity scaling up from a minor species with less than a million individuals to having decisive control over the future. So we should assume that this is possible for new entities whose intelligence vastly exceeds our own.

The case for existential risk from AI is clearly speculative. Yet a speculative case that there is a large risk can be more important than a robust case for a very low-probability risk, such as that posed by asteroids. What we need are ways to judge just how speculative it really is, and a very useful starting point is to hear what those working in the field think about this risk.

There is actually less disagreement here than first appears. Those who counsel caution agree that the timeframe to AGI is decades, not years, and typically suggest research on alignment, not government regulation. So the substantive disagreement is not really over whether AGI is possible or whether it plausibly could be a threat to humanity. It is over whether a potential existential threat that looks to be decades away should be of concern to us now. It seems to me that it should.

The best window into what those working on AI really believe comes from the 2016 survey of leading AI researchers: 70 percent agreed with University of California, Berkeley professor Stuart Russell’s broad argument about why advanced AI with misaligned values might pose a risk; 48 percent thought society should prioritize AI safety research more (only 12 percent thought less). And half the respondents estimated that the probability of the long-term impact of AGI being “extremely bad (e.g. human extinction)” was at least 5 percent.

I find this last point particularly remarkable—in how many other fields would the typical leading researcher think there is a 1 in 20 chance the field’s ultimate goal would be extremely bad for humanity? There is a lot of uncertainty and disagreement, but it is not at all a fringe position that AGI will be developed within 50 years and that it could be an existential catastrophe.

Even though our current and foreseeable systems pose no threat to humanity at large, time is of the essence. In part this is because progress may come very suddenly: Through unpredictable research breakthroughs, or by rapid scaling-up of the first intelligent systems (for example, by rolling them out to thousands of times as much hardware, or allowing them to improve their own intelligence). And in part it is because such a momentous change in human affairs may require more than a couple of decades to adequately prepare for. In the words of Demis Hassabis, co-founder of DeepMind:

We need to use the downtime, when things are calm, to prepare for when things get serious in the decades to come. The time we have now is valuable, and we need to make use of it.

Toby Ord is a philosopher and research fellow at the Future of Humanity Institute, and the author of The Precipice: Existential Risk and the Future of Humanity.

From the book The Precipice by Toby Ord. Copyright © 2020 by Toby Ord. Reprinted by permission of Hachette Books, New York, NY. All rights reserved.

Lead Image: Titima Ongkantong / Shutterstock

When I worked as a TV reporter covering health and science, I would often be recognized in public places. For the most part, the interactions were brief hellos or compliments. Two periods of time stand out when significant numbers of those who approached me were seeking detailed information: the earliest days of the pandemic that became HIV/AIDS and during the anthrax attacks shortly following 9/11. Clearly people feared for their own safety and felt their usual sources of information were not offering them satisfaction. Citizens’ motivation to seek advice when they feel they aren’t getting it from official sources is a strong indication that risk communication is doing a substandard job. It’s significant that one occurred in the pre-Internet era and one after. We can’t blame a public feeling misinformed solely on the noise of the digital age.

America is now opening up from COVID-19 lockdown with different rules in different places. In many parts of the country, people have been demonstrating, even rioting, for restrictions to be lifted sooner. Others are terrified of loosening the restrictions because they see COVID-19 cases and deaths still rising daily. The officials deciding what to open, and when, seldom offer thoughtful rationales. Clearly, risk communication about COVID-19 is failing with potentially dire consequences.

A big part of maintaining credibility is to admit to uncertainty—something politicians are loath to do.

Peter Sandman is a foremost expert on risk communication. A former professor at Rutgers University, he was a top consultant with the Centers for Disease Control in designing crisis and emergency risk-communication, a field of study that combines public health with psychology. Sandman is known for the formula Risk = Hazard + Outrage. His goal is to create better communication about risk, allowing people to assess hazards and not get caught up in outrage at politicians, public health officials, or the media. Today, Sandman is a risk consultant, teamed with his wife, Jody Lanard, a pediatrician and psychiatrist. Lanard wrote the first draft of the World Health Organization’s Outbreak Communications Guidelines. “Jody and Peter are seen as the umpires to judge the gold standard of risk communications,” said Michael Osterholm of the Center for Infectious Disease Research and Policy at the University of Minnesota. Sandman and Lanard have posted a guide for effective COVID-19 communication on the center’s website.

I reached out to Sandman to expand on their advice. We communicated through email.

Sandman began by saying he understood the protests around the country about the lockdown. “It’s very hard to warn people to abide by social-distancing measures when they’re so outraged that they want to kill somebody and trust absolutely nothing people say,” he told me. “COVID-19 outrage taps into preexisting grievances and ideologies. It’s not just about COVID-19 policies. It’s about freedom, equality, too much or too little government. It’s about the arrogance of egghead experts, left versus right, globalism versus nationalism versus federalism. And it’s endlessly, pointlessly about Donald Trump.”

Since the crisis began, Sandman has isolated three categories of grievance. He spelled them out for me, assuming the voices of the outraged:

• “In parts of the country, the response to COVID-19 was delayed and weak; officials unwisely prioritized ‘allaying panic’ instead of allaying the spread of the virus; lockdown then became necessary, not because it was inevitable but because our leaders had screwed up; and now we’re very worried about coming out of lockdown prematurely or chaotically, mishandling the next phase of the pandemic as badly as we handled the first phase.”

• “In parts of the country, the response to COVID-19 was excessive—as if the big cities on the two coasts were the whole country and flyover America didn’t need or didn’t deserve a separate set of policies. There are countless rural counties with zero confirmed cases. Much of the U.S. public-health profession assumes and even asserts without building an evidence-based case that these places, too, needed to be locked down and now need to reopen carefully, cautiously, slowly, and not until they have lots of testing and contact-tracing capacity. How dare they destroy our economy (too) just because of their mishandled outbreak!”

• “Once again the powers-that-be have done more to protect other people’s health than to protect my health. And once again the powers-that-be have done more to protect other people’s economic welfare than to protect my economic welfare!” (These claims can be made with considerable truth by healthcare workers; essential workers in low-income, high-touch occupations; residents of nursing homes; African-Americans; renters who risk eviction; the retired whose savings are threatened; and others.)

In their article for the Center for Infectious Disease Research and Policy, Sandman and Lanard point out that coping with a pandemic requires a thorough plan of communication. This is particularly important as the crisis is likely to enter a second wave of infection, when it could be more devastating. The plan starts with six core principles: 1) Don’t over-reassure, 2) Proclaim uncertainty, 3) Validate emotions—your audience’s and your own, 4) Give people things to do, 5) Admit and apologize for errors, and 6) Share dilemmas. To achieve the first three core principles, officials must immediately share what they know, even if the information may be incomplete. If officials share good news, they must be careful not to make it too hopeful. Over-reassurance is one of the biggest dangers in crisis communication. Sandman and Lanard suggest officials say things like, “Even though the number of new confirmed cases went down yesterday, I don’t want to put too much faith in one day’s good news.” 

Sandman and Lanard say a big part of maintaining credibility is to admit to uncertainty—something politicians are loath to do. They caution against invoking “science” as a sole reason for action, as science in the midst of a crisis is “incremental, fallible, and still in its infancy.” Expressing empathy, provided it’s genuine, is important, Sandman and Lanard say. It makes the bearer more human and believable. A major tool of empathy is to acknowledge the public’s fear as well as your own. There is good reason to be terrified about this virus and its consequences on society. It’s not something to hide.

Sandman and Lanard say current grievances with politicians, health officials, and the media, about how the crisis has been portrayed, have indeed been contradictory. But that makes them no less valid. Denying the contradictions only amplifies divisions in the public and accelerates the outrage, possibly beyond control. They strongly emphasize one piece of advice. “Before we can share the dilemma of how best to manage any loosening of the lockdown, we must decisively—and apologetically—disabuse the public of the myth that, barring a miracle, the COVID-19 pandemic can possibly be nearing its end in the next few months.”

Robert Bazell is an adjunct professor of molecular, cellular, and developmental biology at Yale. For 38 years, he was chief science correspondent for NBC News.

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