Machines of
Loving Grace 1
How AI Could Transform the World for the Better

October 2024

I think and talk a lot about the risks of powerful AI. The company I’m the CEO of, Anthropic, does a lot
of research on how to reduce these risks. Because of this, people sometimes draw the conclusion that I’m
a pessimist or “doomer” who thinks AI will be mostly bad or dangerous. I don’t think that at all. In
fact, one of my main reasons for focusing on risks is that they’re the only thing standing between us
and what I see as a fundamentally positive future. I think that most people are underestimating
just how radical the upside of AI could be, just as I think most people are underestimating
how bad the risks could be.

In this essay I try to sketch out what that upside might look like—what a world with powerful AI might
look like if everything goes right. Of course no one can know the future with any certainty or
precision, and the effects of powerful AI are likely to be even more unpredictable than past
technological changes, so all of this is unavoidably going to consist of guesses. But I am aiming for at
least educated and useful guesses, which capture the flavor of what will happen even if most details end
up being wrong. I’m including lots of details mainly because I think a concrete vision does more to
advance discussion than a highly hedged and abstract one.

First, however, I wanted to briefly explain why I and Anthropic haven’t talked that much about powerful
AI’s upsides, and why we’ll probably continue, overall, to talk a lot about risks. In particular, I’ve
made this choice out of a desire to:

Maximize leverage. The basic development of AI technology and many (not all) of its
benefits seems inevitable (unless the risks derail everything) and is fundamentally driven by
powerful market forces. On the other hand, the risks are not predetermined and our actions can
greatly change their likelihood.
Avoid perception of propaganda. AI companies talking about all the amazing benefits
of AI can come off like propagandists, or as if they’re attempting to distract from downsides. I
also think that as a matter of principle it’s bad for your soul to spend too much of your time
“talking your book”.
Avoid grandiosity. I am often turned off by the way many AI risk public figures
(not to mention AI company leaders) talk about the post-AGI world, as if it’s their mission to
single-handedly bring it about like a prophet leading their people to salvation. I think it’s
dangerous to view companies as unilaterally shaping the world, and dangerous to view practical
technological goals in essentially religious terms.
Avoid “sci-fi” baggage. Although I think most people underestimate the upside of
powerful AI, the small community of people who do discuss radical AI futures often does so in an
excessively “sci-fi” tone (featuring e.g. uploaded minds, space exploration, or general cyberpunk
vibes). I think this causes people to take the claims less seriously, and to imbue them with a sort
of unreality. To be clear, the issue isn’t whether the technologies described are possible or likely
(the main essay discusses this in granular detail)—it’s more that the “vibe” connotatively smuggles
in a bunch of cultural baggage and unstated assumptions about what kind of future is desirable, how
various societal issues will play out, etc. The result often ends up reading like a fantasy for a
narrow subculture, while being off-putting to most people.

Yet despite all of the concerns above, I really do think it’s important to discuss what a good world with
powerful AI could look like, while doing our best to avoid the above pitfalls. In fact I think it is
critical to have a genuinely inspiring vision of the future, and not just a plan to fight fires.
Many of the implications of powerful AI are adversarial or dangerous, but at the end of it all, there
has to be something we’re fighting for, some positive-sum outcome where everyone is better off,
something to rally people to rise above their squabbles and confront the challenges ahead. Fear is one
kind of motivator, but it’s not enough: we need hope as well.

The list of positive applications of powerful AI is extremely long (and includes robotics, manufacturing,
energy, and much more), but I’m going to focus on a small number of areas that seem to me to have the
greatest potential to directly improve the quality of human life. The five categories I am most excited
about are:

Biology and physical health
Neuroscience and mental health
Economic development and poverty
Peace and governance
Work and meaning

My predictions are going to be radical as judged by most standards (other than sci-fi “singularity”
visions 2 ), but I mean them earnestly
and sincerely. Everything I’m saying could very easily be wrong (to repeat my point from above), but
I’ve at least attempted to ground my views in a semi-analytical assessment of how much progress in
various fields might speed up and what that might mean in practice. I am fortunate to have professional
experience in both
biology and neuroscience, and I am an informed amateur in the field of economic development, but
I am sure I will get plenty of things wrong. One thing writing this essay has made me realize is that it
would be valuable to bring together a group of domain experts (in biology, economics, international
relations, and other areas) to write a much better and more informed version of what I’ve produced here.
It’s probably best to view my efforts here as a starting prompt for that group.

Basic assumptions and framework

To make this whole essay more precise and grounded, it’s helpful to specify clearly what we mean by
powerful AI (i.e. the threshold at which the 5-10 year clock starts counting), as well as laying out a
framework for thinking about the effects of such AI once it’s present.

What powerful AI (I dislike the term AGI) 3 will look like, and when (or if) it will arrive, is a huge topic in
itself. It’s one I’ve discussed publicly and could write a completely separate essay on (I probably will
at some point). Obviously, many people are skeptical that powerful AI will be built soon and some are
skeptical that it will ever be built at all. I think it could come as early as 2026, though there are
also ways it could take much longer. But for the purposes of this essay, I’d like to put these issues
aside, assume it will come reasonably soon, and focus on what happens in the 5-10 years after that. I
also want to assume a definition of what such a system will look like, what its capabilities are
and how it interacts, even though there is room for disagreement on this.

By powerful AI, I have in mind an AI model—likely similar to today’s LLM’s in form, though it
might be based on a different architecture, might involve several interacting models, and might be
trained differently—with the following properties:

In terms of pure intelligence 4 , it
is smarter than a Nobel Prize winner across most relevant fields –
biology, programming, math, engineering, writing, etc. This means it can prove unsolved mathematical
theorems, write extremely good novels, write difficult codebases from scratch, etc.
In addition to just being a “smart thing you talk to”, it has all the “interfaces” available to a
human working virtually, including text, audio, video, mouse and keyboard control, and internet
access. It can engage in any actions, communications, or remote operations enabled by this
interface, including taking actions on the internet, taking or giving directions to humans, ordering
materials, directing experiments, watching videos, making videos, and so on. It does all of these
tasks with, again, a skill exceeding that of the most capable humans in the world.
It does not just passively answer questions; instead, it can be given tasks that take hours, days,
or weeks to complete, and then goes off and does those tasks autonomously, in the way a smart
employee would, asking for clarification as necessary.
It does not have a physical embodiment (other than living on a computer screen), but it can control
existing physical tools, robots, or laboratory equipment through a computer; in theory it could even
design robots or equipment for itself to use.
The resources used to train the model can be repurposed to run millions of instances of it
(this matches projected cluster sizes by ~2027), and the model can absorb information and generate
actions at roughly 10x-100x human speed 5 . It may however be limited by the response time of the physical
world or of software it interacts with.
Each of these million copies can act independently on unrelated tasks, or if needed can all work
together in the same way humans would collaborate, perhaps with different subpopulations fine-tuned
to be especially good at particular tasks.

We could summarize this as a “country of geniuses in a datacenter”.

Clearly such an entity would be capable of solving very difficult problems, very fast, but it is not
trivial to figure out how fast. Two “extreme” positions both seem false to me. First, you might think
that the world would be instantly transformed on the scale of seconds or days (“the Singularity”), as superior intelligence builds on itself and solves every
possible scientific, engineering, and operational task almost immediately. The problem with this is that
there are real physical and practical limits, for example around building hardware or conducting
biological experiments. Even a new country of geniuses would hit up against these limits. Intelligence
may be very powerful, but it isn’t magic fairy dust.

Second, and conversely, you might believe that technological progress is saturated or rate-limited by
real world data or by social factors, and that better-than-human intelligence will add very little 6 . This seems equally implausible to
me—I can think of hundreds of scientific or even social problems where a large group of really smart
people would drastically speed up progress, especially if they aren’t limited to analysis and can make
things happen in the real world (which our postulated country of geniuses can, including by directing or
assisting teams of humans).

I think the truth is likely to be some messy admixture of these two extreme pictures, something that
varies by task and field and is very subtle in its details. I believe we need new frameworks to think
about these details in a productive way.

Economists often talk about “factors of production”: things like labor, land, and capital. The phrase
“marginal returns to labor/land/capital” captures the idea that in a given situation, a given factor may
or may not be the limiting one – for example, an air force needs both planes and pilots, and hiring more
pilots doesn’t help much if you’re out of planes. I believe that in the AI age, we should be talking
about the marginal returns to intelligence 7 , and trying to figure out what the other factors are that are
complementary to intelligence and that become limiting factors when intelligence is very high. We are
not used to thinking in this way—to asking “how much does being smarter help with this task, and on what
timescale?”—but it seems like the right way to conceptualize a world with very powerful AI.

My guess at a list of factors that limit or are complementary to intelligence includes:

Speed of the outside world. Intelligent agents need to operate interactively in the
world in order to accomplish things and also to learn 8 . But the world only moves so fast. Cells and animals run at a fixed
speed so experiments on them take a certain amount of time which may be irreducible. The same is
true of hardware, materials science, anything involving communicating with people, and even our
existing software infrastructure. Furthermore, in science many experiments are often needed in
sequence, each learning from or building on the last. All of this means that the speed at which a
major project—for example developing a cancer cure—can be completed may have an irreducible minimum
that cannot be decreased further even as intelligence continues to increase.
Need for data. Sometimes raw data is lacking and in its absence more intelligence
does not help. Today’s particle physicists are very ingenious and have developed a wide range of
theories, but lack the data to choose between them because particle accelerator data is so limited. It is not clear that they would do drastically better if they
were superintelligent—other than perhaps by speeding up the construction of a bigger accelerator.

Intrinsic complexity. Some things are inherently unpredictable or chaotic and even
the most powerful AI cannot predict or untangle them substantially better than a human or a computer
today. For example, even incredibly powerful AI could predict only marginally further ahead in a
chaotic system (such as the three-body problem) in the general case, 9 as compared to today’s humans and computers.

Constraints from humans. Many things cannot be done without breaking laws, harming
humans, or messing up society. An aligned AI would not want to do these things (and if we have an
unaligned AI, we’re back to talking about risks). Many human societal structures are inefficient or
even actively harmful, but are hard to change while respecting constraints like legal requirements
on clinical trials, people’s willingness to change their habits, or the behavior of governments.
Examples of advances that work well in a technical sense, but whose impact has been substantially
reduced by regulations or misplaced fears, include nuclear power, supersonic flight, and even elevators.
Physical laws. This is a starker version of the first point. There are certain
physical laws that appear to be unbreakable. It’s not possible to travel faster than light. Pudding does not unstir.
Chips can only have so many transistors per square centimeter before they become
unreliable. Computation requires a certain minimum
energy per bit erased, limiting the density of computation in the world.

There is a further distinction based on timescales. Things that are hard constraints in the short
run may become more malleable to intelligence in the long run. For example, intelligence might be used
to develop a new experimental paradigm that allows us to learn in vitro what used to require live
animal experiments, or to build the tools needed to collect new data (e.g. the bigger particle
accelerator), or to (within ethical limits) find ways around human-based constraints (e.g. helping to
improve the clinical trial system, helping to create new jurisdictions where clinical trials have less
bureaucracy, or improving the science itself to make human clinical trials less necessary or cheaper).

Thus, we should imagine a picture where intelligence is initially heavily bottlenecked by the other
factors of production, but over time intelligence itself increasingly routes around the other factors,
even if they never fully dissolve (and some things like physical laws are absolute) 10 . The key question is how fast it all happens and
in what order.

With the above framework in mind, I’ll try to answer that question for the five areas mentioned in the
introduction.

1. Biology and health

Biology is probably the area where scientific progress has the greatest potential to directly and
unambiguously improve the quality of human life. In the last century some of the most ancient human
afflictions (such as smallpox) have finally been vanquished, but many more still remain, and defeating
them would be an enormous humanitarian accomplishment. Beyond even curing disease, biological science
can in principle improve the baseline quality of human health, by extending the healthy human
lifespan, increasing control and freedom over our own biological processes, and addressing everyday
problems that we currently think of as immutable parts of the human condition.

In the “limiting factors” language of the previous section, the main challenges with directly applying
intelligence to biology are data, the speed of the physical world, and intrinsic complexity (in fact,
all three are related to each other). Human constraints also play a role at a later stage, when clinical
trials are involved. Let’s take these one by one.

Experiments on cells, animals, and even chemical processes are limited by the speed of the physical
world: many biological protocols involve culturing bacteria or other cells, or simply waiting for
chemical reactions to occur, and this can sometimes take days or even weeks, with no obvious way to
speed it up. Animal experiments can take months (or more) and human experiments often take years (or
even decades for long-term outcome studies). Somewhat related to this, data is often lacking—not so much
in quantity, but quality: there is always a dearth of clear, unambiguous data that isolates a biological
effect of interest from the other 10,000 confounding things that are going on, or that intervenes
causally in a given process, or that directly measures some effect (as opposed to inferring its
consequences in some indirect or noisy way). Even massive, quantitative molecular data, like the
proteomics data that I collected while working on mass spectrometry techniques, is noisy and misses a
lot (which types of cells were these proteins in? Which part of the cell? At what phase in the cell
cycle?).

In part responsible for these problems with data is intrinsic complexity: if you’ve ever seen a diagram showing the biochemistry of human metabolism, you’ll know that it’s very
hard to isolate the effect of any part of this complex system, and even harder to intervene on the
system in a precise or predictable way. And finally, beyond just the intrinsic time that it takes to run
an experiment on humans, actual clinical trials involve a lot of bureaucracy and regulatory requirements
that (in the opinion of many people, including me) add unnecessary additional time and delay progress.

Given all this, many biologists have long been skeptical of the value
of AI and “big data” more generally in biology. Historically, mathematicians, computer scientists, and
physicists who have applied their skills to biology over the last 30 years have been quite successful,
but have not had the truly transformative impact initially hoped for. Some of the skepticism has been
reduced by major and revolutionary breakthroughs like AlphaFold (which has just deservedly won its creators the Nobel Prize in
Chemistry) and AlphaProteo 11 , but there’s still a perception that AI is (and will continue to be)
useful in only a limited set of circumstances. A common formulation is “AI can do a better job analyzing
your data, but it can’t produce more data or improve the quality of the data. Garbage in, garbage out”.

But I think that pessimistic perspective is thinking about AI in the wrong way. If our core hypothesis
about AI progress is correct, then the right way to think of AI is not as a method of data analysis, but
as a virtual biologist who performs all the tasks biologists do, including designing and running
experiments in the real world (by controlling lab robots or simply telling humans which experiments to
run – as a Principal Investigator would to their graduate students), inventing new biological methods or
measurement
techniques, and so on. It is by speeding up the whole research process that AI can truly
accelerate biology. I want to repeat this because it’s the most common misconception that comes
up when I talk about AI’s ability to transform biology: I am not talking about AI as merely a
tool to analyze data. In line with the definition of powerful AI at the beginning of this essay, I’m
talking about using AI to perform, direct, and improve upon nearly everything biologists
do.

To get more specific on where I think acceleration is likely to come from, a surprisingly large fraction
of the progress in biology has come from a truly tiny number of discoveries, often related to broad
measurement tools or techniques 12
that allow precise but generalized or programmable intervention in biological systems. There’s perhaps
~1 of these major discoveries per year and collectively they arguably drive>50% of progress in biology.
These discoveries are so powerful precisely because they cut through intrinsic complexity and data
limitations, directly increasing our understanding and control over biological processes. A few
discoveries per decade have enabled both the bulk of our basic scientific understanding of biology, and
have driven many of the most powerful medical treatments.

Some examples include:

CRISPR: a technique that allows
live editing of any gene in living organisms (replacement of any arbitrary gene sequence with any
other arbitrary sequence). Since the original technique was developed, there have been constant
improvements to target specific cell types, increasing accuracy, and reducing edits of the
wrong gene—all of which are needed for safe use in humans.
Various kinds of microscopy for watching what is going on at a precise level: advanced light
microscopes (with various kinds of fluorescent techniques, special optics, etc), electron
microscopes, atomic force microscopes, etc.
Genome sequencing and synthesis, which has dropped in cost by several orders of magnitude in the last couple decades.

Optogenetic techniques that allow you to get a neuron to fire by shining a
light on it.
mRNA vaccines that, in
principle, allow us to design a vaccine against anything and then quickly adapt it (mRNA vaccines of
course became famous during COVID).
Cell therapies such as CAR-T
that allow immune cells to be taken out of the body and “reprogrammed” to attack, in principle,
anything.
Conceptual insights like the germ theory of disease or the realization of a link between the immune
system and cancer 13 .

I’m going to the trouble of listing all these technologies because I want to make a crucial claim about
them: I think their rate of discovery could be increased by 10x or more if there were a lot more
talented, creative researchers . Or, put another way, I think the returns to
intelligence are high for these discoveries, and that everything else in biology and
medicine mostly follows from them.

Why do I think this? Because of the answers to some questions that we should get in the habit of asking
when we’re trying to determine “returns to intelligence”. First, these discoveries are generally made by
a tiny number of researchers, often the same people repeatedly, suggesting skill and not random search
(the latter might suggest lengthy experiments are the limiting factor). Second, they often “could have
been made” years earlier than they were: for example, CRISPR was a naturally occurring component of the
immune system in bacteria that’s been known since the
80’s, but it took another 25 years for people to realize it could be repurposed for general gene
editing. They also are often delayed many years by lack of support from the scientific community for
promising directions (see this profile on the inventor of mRNA vaccines; similar stories abound). Third,
successful projects are often scrappy or were afterthoughts that people didn’t initially think were
promising, rather than massively funded efforts. This suggests that it’s not just massive resource
concentration that drives discoveries, but ingenuity.

Finally, although some of these discoveries have “serial dependence” (you need to make discovery A first
in order to have the tools or knowledge to make discovery B)—which again might create experimental
delays—many, perhaps most, are independent, meaning many at once can be worked on in parallel. Both
these facts, and my general experience as a biologist, strongly suggest to me that there are hundreds of
these discoveries waiting to be made if scientists were smarter and better at making connections between
the vast amount of biological knowledge humanity possesses (again consider the CRISPR example). The
success of AlphaFold/AlphaProteo at solving important problems much more effectively than humans,
despite decades of carefully designed physics modeling, provides a proof of principle (albeit with a
narrow tool in a narrow domain) that should point the way forward.

Thus, it’s my guess that powerful AI could at least 10x the rate of these discoveries, giving us the next
50-100 years of biological progress in 5-10 years. 14 Why not 100x? Perhaps it is possible, but here both serial dependence
and experiment times become important: getting 100 years of progress in 1 year requires a lot of things
to go right the first time, including animal experiments and things like designing microscopes or
expensive lab facilities. I’m actually open to the (perhaps absurd-sounding) idea that we could get
1000 years of progress in 5-10 years, but very skeptical that we can get 100 years in 1 year.
Another way to put it is I think there’s an unavoidable constant delay: experiments and hardware design
have a certain “latency” and need to be iterated upon a certain “irreducible” number of times in order
to learn things that can’t be deduced logically. But massive parallelism may be possible on top of
that 15 .

What about clinical trials? Although there is a lot of bureaucracy and slowdown associated with them, the
truth is that a lot (though by no means all!) of their slowness ultimately derives from the need to
rigorously evaluate drugs that barely work or ambiguously work. This is sadly true of most therapies
today: the average cancer drug increases survival by a few months while having significant side effects
that need to be carefully measured (there’s a similar story for Alzheimer’s drugs). This leads to huge
studies (in order to achieve statistical power) and difficult tradeoffs which regulatory agencies
generally aren’t great at making, again because of bureaucracy and the complexity of competing
interests.

When something works really well, it goes much faster: there’s an accelerated approval track and the ease
of approval is much greater when effect sizes are larger. mRNA vaccines for COVID were approved in 9
months—much faster than the usual pace. That said, even under these conditions clinical trials are still
too slow—mRNA vaccines arguably should have
been approved in ~2 months. But these kinds of delays (~1 year end-to-end for a drug) combined
with massive parallelization and the need for some but not too much iteration (“a few tries”) are very
compatible with radical transformation in 5-10 years. Even more optimistically, it is possible that AI-enabled biological science will reduce the need for iteration in clinical
trials by developing better animal and cell experimental models (or even simulations) that are more
accurate in predicting what will happen in humans. This will be particularly important in developing
drugs against the aging process, which plays out over decades and where we need a faster iteration loop.

Finally, on the topic of clinical trials and societal barriers, it is worth pointing out explicitly that
in some ways biomedical innovations have an unusually strong track record of being successfully
deployed, in contrast to some other technologies 16 . As mentioned in the introduction, many technologies are hampered by
societal factors despite working well technically. This might suggest a pessimistic perspective on what
AI can accomplish. But biomedicine is unique in that although the process of developing drugs is
overly cumbersome, once developed they generally are successfully deployed and used.

To summarize the above, my basic prediction is that AI-enabled biology and medicine will allow us to
compress the progress that human biologists would have achieved over the next 50-100 years into 5-10
years. I’ll refer to this as the “compressed 21st century”: the idea that after powerful AI is
developed, we will in a few years make all the progress in biology and medicine that we would have made
in the whole 21st century.

Although predicting what powerful AI can do in a few years remains inherently difficult and
speculative,
there is some concreteness to asking “what could humans do unaided in the next 100 years?”. Simply
looking at what we’ve accomplished in the 20th century, or extrapolating from the first 2 decades of
the
21st, or asking what “10 CRISPR’s and 50 CAR-T’s” would get us, all offer practical, grounded ways
to
estimate the general level of progress we might expect from powerful AI.

Below I try to make a list of what we might expect. This is not based on any rigorous methodology,
and
will almost certainly prove wrong in the details, but it’s trying to get across the general
level
of radicalism we should expect:

Reliable prevention and treatment of nearly all 17 natural infectious
disease.
Given the enormous advances against infectious disease in the 20th century, it is not radical to
imagine that we could more or less “finish the job” in a compressed 21st. mRNA vaccines and
similar
technology already point the way towards “vaccines for anything”. Whether infectious disease is fully
eradicated
from the world (as opposed to just in some places) depends on questions about poverty
and
inequality, which are discussed in Section 3.
Elimination of most cancer. Death rates from cancer have been dropping
~2%
per year for the last few decades; thus we are on track to eliminate most cancer in the
21st
century at the current pace of human science. Some subtypes have already been largely cured (for
example some types of leukemia with CAR-T therapy), and I’m perhaps even more excited for very selective
drugs
that target cancer in its infancy and prevent it
from ever growing. AI will also make possible treatment regimens very finely adapted to the individualized genome of the cancer—these are
possible
today, but hugely expensive in time and human expertise, which AI should allow us to scale.
Reductions of 95% or more in both mortality and incidence seem possible. That said, cancer is
extremely varied and adaptive, and is likely the hardest of these diseases to fully destroy. It
would not be surprising if an assortment of rare, difficult malignancies persists.
Very effective prevention and effective cures for genetic disease. Greatly
improved
embryo
screening
will likely make it possible to prevent most genetic disease, and some safer, more reliable
descendant of CRISPR may cure most genetic disease in existing people. Whole-body afflictions
that
affect a large fraction of cells may be the last holdouts, however.

Prevention of Alzheimer’s. We’ve had a very hard time figuring out what causes
Alzheimer’s (it is somehow related to beta-amyloid protein, but the actual details seem to be very complex).
It
seems like exactly the type of problem that can be solved with better measurement tools that
isolate
biological effects; thus I am bullish about AI’s ability to solve it. There is a good chance it
can
eventually be prevented with relatively simple interventions, once we actually understand what
is
going on. That said, damage from already-existing Alzheimer’s may be very difficult to reverse.

Improved treatment of most other ailments. This is a catch-all category for
other
ailments including diabetes, obesity, heart disease, autoimmune diseases, and more. Most of
these
seem “easier” to solve than cancer and Alzheimer’s and in many cases are already in steep
decline.
For example, deaths from heart disease have already declined over 50%, and simple interventions
like
GLP-1 agonists have already made huge progress against obesity and
diabetes.

Biological freedom. The last 70 years featured advances in birth control,
fertility, management of weight, and much more. But I suspect AI-accelerated
biology
will greatly expand what is possible: weight, physical appearance, reproduction, and other
biological processes will be fully under people’s control. We’ll refer to these under the
heading of
biological freedom: the idea that everyone should be empowered to choose what they want
to
become and live their lives in the way that most appeals to them. There will of course be
important
questions about global equality of access; see Section 3 for these.

Doubling of the human lifespan 18 .This might seem radical,
but life expectancy increased
almost 2x in the 20th century (from ~40 years to ~75), so it’s “on trend” that the
“compressed 21st” would double it again to 150. Obviously the interventions involved in slowing
the
actual aging process will be different from those that were needed in the last century to
prevent
(mostly childhood) premature deaths from disease, but the magnitude of change is not
unprecedented 19 . Concretely,
there already exist
drugs that increase maximum lifespan in rats by 25-50% with limited ill-effects. And
some
animals (e.g. some types of turtle) already live 200 years, so humans are manifestly not at some
theoretical upper limit. At a guess, the most important thing that is needed might be reliable,
non-Goodhart-able
biomarkers of human aging, as that will allow fast iteration on experiments and clinical trials.
Once human lifespan is 150, we may be able to reach “escape velocity”, buying enough time that
most
of those currently alive today will be able to live as long as they want, although there’s
certainly
no guarantee this is biologically possible.

It is worth looking at this list and reflecting on how different the world will be if all of it is
achieved 7-12 years from now (which would be in line with an aggressive AI timeline). It goes
without
saying that it would be an unimaginable humanitarian triumph, the elimination all at once of most of
the
scourges that have haunted humanity for millennia. Many of my friends and colleagues are raising
children, and when those children grow up, I hope that any mention of disease will sound to them the
way
scurvy, smallpox, or bubonic
plague
sounds to us. That generation will also benefit from increased biological freedom and
self-expression,
and with luck may also be able to live as long as they want.

It’s hard to overestimate how surprising these changes will be to everyone except the small community
of
people who expected powerful AI. For example, thousands of economists and policy experts in the US
currently debate how to keep Social Security and Medicare solvent, and more broadly how to
keep
down the cost of healthcare (which is mostly consumed by those over 70 and especially those with
terminal illnesses such as cancer). The situation for these programs is likely to be radically
improved
if all this comes to pass 20 , as
the
ratio of working age to retired population will change drastically. No doubt these challenges will
be
replaced with others, such as how to ensure widespread access to the new technologies, but it is
worth
reflecting on how much the world will change even if biology is the only area to be
successfully
accelerated by AI.

2. Neuroscience and mind

In the previous section I focused on physical diseases and biology in general, and didn’t
cover
neuroscience or mental health. But neuroscience is a subdiscipline of biology and mental health is
just
as important as physical health. In fact, if anything, mental health affects human well-being even
more
directly than physical health. Hundreds of millions of people have very low quality of life due to
problems like addiction, depression, schizophrenia, low-functioning autism, PTSD, psychopathy 21 , or intellectual disabilities.
Billions more struggle with everyday problems that can often be interpreted as much milder versions
of
one of these severe clinical disorders. And as with general biology, it may be possible to go beyond
addressing problems to improving the baseline quality of human experience.

The basic framework that I laid out for biology applies equally to neuroscience. The field is
propelled
forward by a small number of discoveries often related to tools for measurement or precise
intervention
– in the list of those above, optogenetics was a neuroscience discovery, and more recently CLARITY and expansion microscopy are
advances
in the same vein, in addition to many of the general cell biology methods directly carrying over to
neuroscience. I think the rate of these advances will be similarly accelerated by AI and therefore
that
the framework of “100 years of progress in 5-10 years” applies to neuroscience in the same way it
does
to biology and for the same reasons. As in biology, the progress in 20th century neuroscience was
enormous – for example we didn’t even understand how or why neurons fired until the
1950’s. Thus, it seems reasonable to expect AI-accelerated neuroscience to produce rapid
progress over a few years.

There is one thing we should add to this basic picture, which is that some of the things we’ve
learned
(or are learning) about AI itself in the last few years are likely to help advance neuroscience,
even if
it continues to be done only by humans. Interpretability is an obvious example: although biological neurons
superficially operate in a completely different manner from artificial neurons (they communicate via
spikes and often spike rates, so there is a time element not present in artificial neurons, and a
bunch
of details relating to cell physiology and neurotransmitters modifies their operation
substantially),
the basic question of “how do distributed, trained networks of simple units that perform combined
linear/non-linear operations work together to perform important computations” is the same, and I
strongly suspect the details of individual neuron communication will be abstracted away in most of
the
interesting questions about computation and circuits 22 . As just one example of this, a computational
mechanism discovered by interpretability researchers in AI systems was recently rediscovered
in
the brains of mice.

It is much easier to do experiments on artificial neural networks than on real ones (the latter often
requires cutting into animal brains), so interpretability may well become a tool for improving our
understanding of neuroscience. Furthermore, powerful AI’s will themselves probably be able to
develop
and apply this tool better than humans can.

Beyond just interpretability though, what we have learned from AI about how intelligent systems are
trained should (though I am not sure it has yet) cause a revolution in neuroscience.
When
I was working in neuroscience, a lot of people focused on what I would now consider the wrong
questions
about learning, because the concept of the scaling hypothesis / bitter lesson didn’t
exist yet. The idea that a simple objective function plus a lot of data can
drive
incredibly complex behaviors makes it more interesting to understand the objective functions and
architectural biases and less interesting to understand the details of the emergent computations. I
have
not followed the field closely in recent years, but I have a vague sense that computational
neuroscientists have still not fully absorbed the lesson. My attitude to the scaling hypothesis has
always been “aha – this is an explanation, at a high level, of how intelligence works and how it so
easily evolved”, but I don’t think that’s the average neuroscientist’s view, in part because the
scaling
hypothesis as “the secret to intelligence” isn’t fully accepted even within AI.

I think that neuroscientists should be trying to combine this basic insight with the particularities
of
the human brain (biophysical limitations, evolutionary history, topology, details of motor and
sensory
inputs/outputs) to try to figure out some of neuroscience’s key puzzles. Some likely are, but I
suspect
it’s not enough yet, and that AI neuroscientists will be able to more effectively leverage this
angle to
accelerate progress.

I expect AI to accelerate neuroscientific progress along four distinct routes, all of which can
hopefully
work together to cure mental illness and improve function:

Traditional molecular biology, chemistry, and genetics. This is essentially the
same story as general biology in section 1, and AI can likely speed it up via the same
mechanisms.
There are many drugs that modulate neurotransmitters in order to alter brain function, affect
alertness or perception, change mood, etc., and AI can help us invent
many more. AI can probably also accelerate research on the genetic basis of mental illness.
Fine-grained neural measurement and intervention. This is the ability to
measure
what a lot of individual neurons or neuronal circuits are doing, and intervene to change their
behavior. Optogenetics and neural probes are technologies capable of both measurement and
intervention in live organisms, and a number of very advanced methods (such as molecular ticker
tapes to read out the firing patterns of large numbers of individual neurons) have also been proposed and seem
possible in principle.
Advanced computational neuroscience. As noted above, both the specific insights
and
the gestalt of modern AI can probably be applied fruitfully to questions in systems neuroscience, including perhaps uncovering the real
causes
and dynamics of complex diseases like psychosis or mood disorders.
Behavioral interventions. I haven’t much mentioned it given the focus on the
biological side of neuroscience, but psychiatry and psychology have of course developed a wide
repertoire of behavioral interventions over the 20th century; it stands to reason that
AI
could accelerate these as well, both the development of new methods and helping patients to
adhere
to existing methods. More broadly, the idea of an “AI coach” who always helps you to be the best
version of yourself, who studies your interactions and helps you learn to be more effective,
seems
very promising.

It’s my guess that these four routes of progress working together would, as with physical disease, be
on
track to lead to the cure or prevention of most mental illness in the next 100 years even if AI was
not
involved – and thus might reasonably be completed in 5-10 AI-accelerated years. Concretely my guess
at
what will happen is something like:

Most mental illness can probably be cured. I’m not an expert in psychiatric
disease
(my time in neuroscience was spent building probes to study small groups of neurons) but it’s my
guess that diseases like PTSD, depression, schizophrenia, addiction, etc. can be figured out and
very effectively treated via some combination of the four directions above. The answer is likely
to
be some combination of “something went wrong biochemically” (although it could be very complex)
and
“something went wrong with the neural network, at a high level”. That is, it’s a systems
neuroscience question—though that doesn’t gainsay the impact of the behavioral interventions
discussed above. Tools for measurement and intervention, especially in live humans, seem likely
to
lead to rapid iteration and progress.
Conditions that are very “structural” may be more difficult, but not
impossible.
There’s some
evidence that psychopathy is associated with obvious neuroanatomical differences – that
some
brain regions are simply smaller or less developed in psychopaths. Psychopaths are also believed
to
lack empathy from a young age; whatever is different about their brain, it was probably always
that
way. The same may be true of some intellectual disabilities, and perhaps other conditions.
Restructuring the brain sounds hard, but it also seems like a task with high returns to
intelligence. Perhaps there is some way to coax the adult brain into an earlier or more plastic
state where it can be reshaped. I’m very uncertain how possible this is, but my instinct is to
be
optimistic about what AI can invent here.
Effective genetic prevention of mental illness seems possible. Most mental
illness
is partially
heritable, and genome-wide association studies are starting
to
gain traction on identifying the relevant factors, which are often many in number. It
will
probably be possible to prevent most of these diseases via embryo screening, similar to the
story
with physical disease. One difference is that psychiatric disease is more likely to be polygenic
(many genes contribute), so due to complexity there’s an increased risk of unknowingly selecting
against positive traits that are correlated with disease. Oddly however, in
recent
years GWAS studies seem to suggest that these correlations might have been overstated. In any case, AI-accelerated
neuroscience may help us to figure these things out. Of course, embryo screening for complex
traits
raises a number of societal issues and will be controversial, though I would guess that most
people
would support screening for severe or debilitating mental illness.
Everyday problems that we don’t think of as clinical disease will also be
solved.
Most of us have everyday psychological problems that are not ordinarily thought of as rising to
the
level of clinical disease. Some people are quick to anger, others have trouble focusing or are
often
drowsy, some are fearful or anxious, or react badly to change. Today, drugs already exist to
help
with e.g. alertness or focus (caffeine, modafinil, ritalin) but as with many other previous
areas,
much more is likely to be possible. Probably many more such drugs exist and have not been
discovered, and there may also be totally new modalities of intervention, such as targeted light
stimulation (see optogenetics above) or magnetic fields. Given how many drugs we’ve developed in
the
20th century that tune cognitive function and emotional state, I’m very optimistic about the
“compressed 21st” where everyone can get their brain to behave a bit better and have a more
fulfilling day-to-day experience.
Human baseline experience can be much better. Taking one step further, many
people
have experienced extraordinary moments of revelation, creative inspiration, compassion,
fulfillment,
transcendence, love, beauty, or meditative peace. The character and frequency of these
experiences
differs greatly from person to person and within the same person at different times, and can
also
sometimes be triggered by various drugs (though often with side effects). All of this suggests
that
the “space of what is possible to experience” is very broad and that a larger fraction of
people’s
lives could consist of these extraordinary moments. It is probably also possible to improve
various
cognitive functions across the board. This is perhaps the neuroscience version of “biological
freedom” or “extended lifespans”.

One topic that often comes up in sci-fi depictions of AI, but that I intentionally haven’t discussed
here, is “mind uploading”, the idea of capturing the pattern and dynamics of a human brain and
instantiating them in software. This topic could be the subject of an essay all by itself, but
suffice
it to say that while I think uploading is almost certainly possible
in
principle, in practice it faces significant technological and societal challenges, even with
powerful
AI, that likely put it outside the 5-10 year window we are discussing.

In summary, AI-accelerated neuroscience is likely to vastly improve treatments for, or even cure,
most
mental illness as well as greatly expand “cognitive and mental freedom” and human cognitive and
emotional abilities. It will be every bit as radical as the improvements in physical health
described in
the previous section. Perhaps the world will not be visibly different on the outside, but the world
as
experienced by humans will be a much better and more humane place, as well as a place that offers
greater opportunities for self-actualization. I also suspect that improved mental health will
ameliorate
a lot of other societal problems, including ones that seem political or economic.

3. Economic development and poverty

The previous two sections are about developing new technologies that cure disease and improve
the
quality of human life. However an obvious question, from a humanitarian perspective, is: “will
everyone
have access to these technologies?”

It is one thing to develop a cure for a disease, it is another thing to eradicate the disease from
the
world. More broadly, many existing health interventions have not yet been applied everywhere in the
world, and for that matter the same is true of (non-health) technological improvements in general.
Another way to say this is that living standards in many parts of the world are still desperately
poor:
GDP per capita is
~$2,000 in Sub-Saharan Africa as compared to ~$75,000 in the United States. If AI further increases
economic growth and quality of life in the developed world, while doing little to help the
developing
world, we should view that as a terrible moral failure and a blemish on the genuine humanitarian
victories in the previous two sections. Ideally, powerful AI should help the developing world
catch
up to the developed world, even as it revolutionizes the latter.

I am not as confident that AI can address inequality and economic growth as I am that it can invent
fundamental technologies, because technology has such obvious high returns to intelligence
(including
the ability to route around complexities and lack of data) whereas the economy involves a lot of
constraints from humans, as well as a large dose of intrinsic complexity. I am somewhat skeptical
that
an AI could solve the famous “socialist calculation problem” 23 and I don’t think governments will (or should) turn over their
economic policy to such an entity, even if it could do so. There are also problems like how to
convince
people to take treatments that are effective but that they may be suspicious of.

The challenges facing the developing world are made even more complicated by pervasive corruption in both
private and public sectors. Corruption creates a vicious cycle: it exacerbates poverty, and
poverty in
turn breeds more corruption. AI-driven plans for economic development need to reckon with corruption,
weak institutions, and other very human challenges.

Nevertheless, I do see significant reasons for optimism. Diseases have been eradicated and many
countries
have gone from poor to rich, and it is clear that the decisions involved in these tasks exhibit
high
returns to intelligence (despite human constraints and complexity). Therefore, AI can likely do them
better than they are currently being done. There may also be targeted interventions that get around the
human constraints and that AI could focus on. More importantly though, we have to try. Both AI
companies
and developed world policymakers will need to do their part to ensure that the developing world is not
left out; the moral imperative is too great. So in this section, I’ll continue to make the optimistic
case, but keep in mind everywhere that success is not guaranteed and depends on our collective efforts.

Below I make some guesses about how I think things may go in the developing world over the 5-10 years
after powerful AI is developed:

Distribution of health interventions. The area where I am perhaps most
optimistic
is distributing health interventions throughout the world. Diseases have actually been
eradicated by
top-down campaigns: smallpox was fully eliminated in the 1970’s, and polio and guinea worm are nearly
eradicated with less than 100 cases per year. Mathematically sophisticated epidemiological modeling plays an active
role
in disease eradication campaigns, and it seems very likely that there is room for
smarter-than-human
AI systems to do a better job of it than humans are. The logistics of distribution can probably
also
be greatly optimized. One thing I learned as an early donor to GiveWell is that some health charities
are way more effective than others;
the hope is that AI-accelerated efforts would be more effective still. Additionally, some
biological
advances actually make the logistics of distribution much easier: for example, malaria has been
difficult to eradicate because it requires treatment each time the disease is contracted; a
vaccine
that only needs to be administered once makes the logistics much simpler (and such vaccines for
malaria are in fact
currently being developed). Even simpler distribution mechanisms are possible: some
diseases
could in principle be eradicated by targeting their animal carriers, for example releasing
mosquitoes infected with a bacterium that blocks their ability to carry a disease (who then infect all the other
mosquitos) or simply using gene drives to wipe out the mosquitos. This requires one or a few
centralized actions, rather than a coordinated campaign that must individually treat millions.
Overall, I think 5-10 years is a reasonable timeline for a good fraction (maybe 50%) of
AI-driven
health benefits to propagate to even the poorest countries in the world. A good goal might be
for
the developing world 5-10 years after powerful AI to at least be substantially healthier than
the
developed world is today, even if it continues to lag behind the developed world. Accomplishing
this
will of course require a huge effort in global health, philanthropy, political advocacy, and
many
other efforts, which both AI developers and policymakers should help with.
Economic growth. Can the developing world quickly catch up to the developed
world,
not just in health, but across the board economically? There is some precedent for this: in the
final decades of the 20th century, several East Asian economies achieved sustained ~10% annual real GDP
growth
rates, allowing them to catch up with the developed world. Human economic planners made the
decisions that led to this success, not by directly controlling entire economies but by pulling
a
few key levers (such as an industrial policy of export-led growth, and resisting the temptation
to
rely on natural resource wealth); it’s plausible that “AI finance ministers and central bankers”
could replicate or exceed this 10% accomplishment. An important question is how to get
developing
world governments to adopt them while respecting the principle of self-determination—some may be
enthusiastic about it, but others are likely to be skeptical. On the optimistic side, many of
the
health interventions in the previous bullet point are likely to organically increase economic
growth: eradicating AIDS/malaria/parasitic worms would have a transformative effect on
productivity,
not to mention the economic benefits that some of the neuroscience interventions (such as
improved
mood and focus) would have in developed and developing world alike. Finally, non-health
AI-accelerated technology (such as energy technology, transport drones, improved building
materials,
better logistics and distribution, and so on) may simply permeate the world naturally; for
example,
even cell phones quickly permeated sub-Saharan Africa via market mechanisms, without needing
philanthropic efforts. On the more negative side, while AI and automation have many potential
benefits, they also pose challenges for economic development, particularly for countries that
haven’t yet industrialized. Finding ways to ensure these countries can still develop and improve
their economies in an age of increasing automation is an important challenge for economists and
policymakers to address. Overall, a dream scenario—perhaps a goal to aim for—would be 20% annual
GDP
growth rate in the developing world, with 10% each coming from AI-enabled economic decisions and
the
natural spread of AI-accelerated technologies, including but not limited to health. If achieved,
this would bring sub-Saharan Africa to the current per-capita GDP of China in 5-10 years, while
raising much of the rest of the developing world to levels higher than the current US GDP.
Again,
this is a dream scenario, not what happens by default: it’s something all of us must work
together
to make more likely.
Food security 24 . Advances in crop technology like better
fertilizers and
pesticides, more automation, and more efficient land use drastically increased crop yields across the
20th
Century, saving millions of people from hunger. Genetic engineering is currently improving many crops even further. Finding even more ways to
do
this—as well as to make agricultural supply chains even more efficient—could give us an
AI-driven
second Green
Revolution, helping close the gap between the developing and developed world.
Mitigating climate change. Climate change will be felt much more strongly in
the
developing world, hampering its development. We can expect that AI will lead to improvements in
technologies that slow or prevent climate change, from atmospheric carbon-removal
and
clean energy technology to lab-grown meat that reduces our reliance on carbon-intensive factory
farming. Of course, as discussed above, technology isn’t the only thing restricting progress on
climate change—as with all of the other issues discussed in this essay, human societal factors
are
important. But there’s good reason to think that AI-enhanced research will give us the means to
make
mitigating climate change far less costly and disruptive, rendering many of the objections moot
and
freeing up developing countries to make more economic progress.
Inequality within countries. I’ve mostly talked about inequality as a global
phenomenon (which I do think is its most important manifestation), but of course inequality also
exists within countries. With advanced health interventions and especially radical
increases
in lifespan or cognitive enhancement drugs, there will certainly be valid worries that these
technologies are “only for the rich”. I am more optimistic about within-country inequality
especially in the developed world, for two reasons. First, markets function better in the
developed
world, and markets are typically good at bringing down the cost of high-value technologies over
time 25 . Second, developed
world
political institutions are more responsive to their citizens and have greater state capacity to
execute universal access programs—and I expect citizens to demand access to technologies that so
radically improve quality of life. Of course it’s not predetermined that such demands
succeed—and
here is another place where we collectively have to do all we can to ensure a fair society.
There is
a separate problem in inequality of wealth (as opposed to inequality of access to
life-saving
and life-enhancing technologies), which seems harder and which I discuss in Section 5.
The opt-out problem. One concern in both developed and developing world alike
is
people opting out of AI-enabled benefits (similar to the anti-vaccine movement, or
Luddite
movements more generally). There could end up being bad feedback cycles where, for example, the
people who are least able to make good decisions opt out of the very technologies that improve
their
decision-making abilities, leading to an ever-increasing gap and even creating a dystopian
underclass (some researchers have argued that this will undermine
democracy, a topic I discuss further in the next section). This would, once again, place
a
moral blemish on AI’s positive advances. This is a difficult problem to solve as I don’t think
it is
ethically okay to coerce people, but we can at least try to increase people’s scientific
understanding—and perhaps AI itself can help us with this. One hopeful sign is that historically
anti-technology movements have been more bark than bite: railing against modern technology is
popular, but most people adopt it in the end, at least when it’s a matter of individual choice.
Individuals tend to adopt most health and consumer technologies, while technologies that are
truly
hampered, like nuclear power, tend to be collective political decisions.

Overall, I am optimistic about quickly bringing AI’s biological advances to people in the developing
world. I am hopeful, though not confident, that AI can also enable unprecedented economic growth
rates
and allow the developing world to at least surpass where the developed world is now. I am concerned
about the “opt out” problem in both the developed and developing world, but suspect that it will
peter
out over time and that AI can help accelerate this process. It won’t be a perfect world, and those
who
are behind won’t fully catch up, at least not in the first few years. But with strong efforts on our
part, we may be able to get things moving in the right direction—and fast. If we do, we can make at
least a downpayment on the promises of dignity and equality that we owe to every human being on
earth.

4. Peace and governance

Suppose that everything in the first three sections goes well: disease, poverty, and inequality are
significantly reduced and the baseline of human experience is raised substantially. It does not
follow
that all major causes of human suffering are solved. Humans are still a threat to each other.
Although there is a trend of technological improvement and economic development leading to
democracy and peace, it is a very loose trend, with frequent (and recent) backsliding. At the dawn of the 20th Century, people thought they had put
war
behind them; then came the two world wars. Thirty years ago Francis Fukuyama wrote about “the End
of
History” and a final triumph of liberal democracy; that hasn’t happened yet. Twenty years
ago US
policymakers believed that free trade with China would cause it to liberalize as it became richer;
that
very much didn’t happen, and we now seem headed for
a
second cold war with a resurgent authoritarian bloc. And plausible theories suggest that
internet technology may actually advantage authoritarianism, not democracy as initially believed
(e.g. in the “Arab Spring” period). It seems important to try to understand how powerful AI will
intersect with these issues of peace, democracy, and freedom.

Unfortunately, I see no strong reason to believe AI will preferentially or structurally advance
democracy
and peace, in the same way that I think it will structurally advance human health and alleviate
poverty.
Human conflict is adversarial and AI can in principle help both the “good guys” and the “bad guys”.
If
anything, some structural factors seem worrying: AI seems likely to enable much better propaganda
and
surveillance, both major tools in the autocrat’s toolkit. It’s therefore up to us as individual
actors
to tilt things in the right direction: if we want AI to favor democracy and individual rights, we
are
going to have to fight for that outcome. I feel even more strongly about this than I do about
international inequality: the triumph of liberal democracy and political stability is not
guaranteed, perhaps not even likely, and will require great sacrifice and commitment on all of our
parts, as it often has in the past.

I think of the issue as having two parts: international conflict, and the internal structure of
nations.
On the international side, it seems very important that democracies have the upper hand on the world
stage when powerful AI is created. AI-powered authoritarianism seems too terrible to contemplate, so
democracies need to be able to set the terms by which powerful AI is brought into the world, both to
avoid being overpowered by authoritarians and to prevent human rights abuses within authoritarian
countries.

My current guess at the best way to do this is via an “entente strategy” 26 , in which a coalition of democracies seeks
to
gain a clear advantage (even just a temporary one) on powerful AI by securing its supply chain,
scaling
quickly, and blocking or delaying adversaries’ access to key resources like chips and
semiconductor equipment. This coalition would on one hand use AI to achieve robust military
superiority
(the stick) while at the same time offering to distribute the benefits of powerful AI (the carrot)
to a
wider and wider group of countries in exchange for supporting the coalition’s strategy to promote
democracy (this would be a bit analogous to “Atoms for Peace”). The coalition would aim to gain the support of more and
more
of the world, isolating our worst adversaries and eventually putting them in a position where they
are
better off taking the same bargain as the rest of the world: give up competing with democracies in
order
to receive all the benefits and not fight a superior foe.

If we can do all this, we will have a world in which democracies lead on the world stage and have the
economic and military strength to avoid being undermined, conquered, or sabotaged by autocracies,
and
may be able to parlay their AI superiority into a durable advantage. This could optimistically lead
to
an “eternal 1991”—a world where democracies have the upper hand and Fukuyama’s dreams are realized.
Again, this will be very difficult to achieve, and will in particular require close cooperation
between
private AI companies and democratic governments, as well as extraordinarily wise decisions about the
balance between carrot and stick.

Even if all that goes well, it leaves the question of the fight between democracy and autocracy
within each country. It is obviously hard to predict what will happen here, but I do have
some
optimism that given a global environment in which democracies control the most powerful AI,
then AI may actually structurally favor democracy everywhere. In particular, in this
environment
democratic governments can use their superior AI to win the information war: they can counter
influence
and propaganda operations by autocracies and may even be able to create a globally free information
environment by providing channels of information and AI services in a way that autocracies lack the
technical ability to block or monitor. It probably isn’t necessary to deliver propaganda, only to
counter malicious attacks and unblock the free flow of information. Although not immediate, a level
playing field like this stands a good chance of gradually tilting global governance towards
democracy,
for several reasons.

First, the increases in quality of life in Sections 1-3 should, all things equal, promote democracy:
historically they have, to at least some extent. In particular I expect improvements in mental
health,
well-being, and education to increase democracy, as all three are negatively
correlated with support for authoritarian leaders. In general people want
more
self-expression when their other needs are met, and democracy is among other things a form of
self-expression. Conversely, authoritarianism thrives on fear and resentment.

Second, there is a good chance free information really does undermine authoritarianism, as long as
the
authoritarians can’t censor it. And uncensored AI can also bring individuals powerful tools for
undermining repressive governments. Repressive governments survive by denying people a certain kind
of
common knowledge, keeping them from realizing that “the emperor has no clothes”. For example Srđa
Popović, who helped to topple the Milošević government in Serbia, has written extensively
about
techniques for psychologically robbing authoritarians of their power, for breaking the spell and
rallying support against a dictator. A superhumanly effective AI version of Popović (whose skills
seem
like they have high returns to intelligence) in everyone’s pocket, one that dictators are powerless
to
block or censor, could create a wind at the backs of dissidents and reformers across the world. To
say
it again, this will be a long and protracted fight, one where victory is not assured, but if we
design
and build AI in the right way, it may at least be a fight where the advocates of freedom everywhere
have
an advantage.

As with neuroscience and biology, we can also ask how things could be “better than normal”—not just
how
to avoid autocracy, but how to make democracies better than they are today. Even within democracies,
injustices happen all the time. Rule-of-law societies make a promise to their citizens that everyone
will be equal under the law and everyone is entitled to basic human rights, but obviously people do
not
always receive those rights in practice. That this promise is even partially fulfilled makes it
something to be proud of, but can AI help us do better?

For example, could AI improve our legal and judicial system by making decisions and processes more
impartial? Today people mostly worry in legal or judicial contexts that AI systems will be a cause of discrimination, and these worries are important and need to
be
defended against. At the same time, the vitality of democracy depends on harnessing new technologies
to
improve democratic institutions, not just responding to risks. A truly mature and successful
implementation of AI has the potential to reduce bias and be fairer for everyone.

For centuries, legal systems have faced the dilemma that the law aims to be impartial, but is
inherently
subjective and thus must be interpreted by biased humans. Trying to make the law fully mechanical
hasn’t
worked because the real world is messy and can’t always be captured in mathematical formulas.
Instead
legal systems rely on notoriously imprecise criteria like “cruel and
unusual
punishment” or “utterly without redeeming social importance”, which humans then
interpret—and
often do so in a manner that displays bias, favoritism, or arbitrariness. “Smart contracts” in
cryptocurrencies haven’t revolutionized law because ordinary code isn’t smart enough to adjudicate
all
that much of interest. But AI might be smart enough for this: it is the first technology capable of
making broad, fuzzy judgements in a repeatable and mechanical way.

I am not suggesting that we literally replace judges with AI systems, but the combination of
impartiality
with the ability to understand and process messy, real world situations feels like it should
have
some serious positive applications to law and justice. At the very least, such systems could work
alongside humans as an aid to decision-making. Transparency would be important in any such system,
and a
mature science of AI could conceivably provide it: the training process for such systems could be
extensively studied, and advanced
interpretability techniques could be used to see inside the final model and assess it for
hidden
biases, in a way that is simply not possible with humans. Such AI tools could also be used to
monitor
for violations of fundamental rights in a judicial or police context, making constitutions more
self-enforcing.

In a similar vein, AI could be used to both aggregate opinions and drive consensus among citizens,
resolving conflict, finding common ground, and seeking compromise. Some early ideas in this
direction
have been undertaken by the computational
democracy
project, including collaborations with Anthropic. A more informed and thoughtful citizenry
would
obviously strengthen democratic institutions.

There is also a clear opportunity for AI to be used to help provision government services—such as
health
benefits or social services—that are in principle available to everyone but in practice often
severely
lacking, and worse in some places than others. This includes health services, the DMV, taxes, social
security, building code enforcement, and so on. Having a very thoughtful and informed AI whose job
is to
give you everything you’re legally entitled to by the government in a way you can understand—and who
also helps you comply with often confusing government rules—would be a big deal. Increasing state
capacity both helps to deliver on the promise of equality under the law, and strengthens respect for
democratic governance. Poorly implemented services are currently a major driver of cynicism about
government 27 .

All of these are somewhat vague ideas, and as I said at the beginning of this section, I am not
nearly as
confident in their feasibility as I am in the advances in biology, neuroscience, and poverty
alleviation. They may be unrealistically utopian. But the important thing is to have an ambitious
vision, to be willing to dream big and try things out. The vision of AI as a guarantor of liberty,
individual rights, and equality under the law is too powerful a vision not to fight for. A 21st
century,
AI-enabled polity could be both a stronger protector of individual freedom, and a beacon of hope
that
helps make liberal democracy the form of government that the whole world wants to adopt.

5. Work and meaning

Even if everything in the preceding four sections goes well—not only do we alleviate disease,
poverty,
and inequality, but liberal democracy becomes the dominant form of government, and existing liberal
democracies become better versions of themselves—at least one important question still remains.
“It’s
great we live in such a technologically advanced world as well as a fair and decent one”, someone
might
object, “but with AI’s doing everything, how will humans have meaning? For that matter, how will
they
survive economically?”.

I think this question is more difficult than the others. I don’t mean that I am necessarily more
pessimistic about it than I am about the other questions (although I do see challenges). I mean that
it
is fuzzier and harder to predict in advance, because it relates to macroscopic questions about how
society is organized that tend to resolve themselves only over time and in a decentralized manner.
For
example, historical hunter-gatherer societies might have imagined that life is meaningless without
hunting and various kinds of hunting-related religious rituals, and would have imagined that our
well-fed technological society is devoid of purpose. They might also have not understood how our
economy
can provide for everyone, or what function people can usefully service in a mechanized society.

Nevertheless, it’s worth saying at least a few words, while keeping in mind that the brevity of this
section is not at all to be taken as a sign that I don’t take these issues seriously—on the
contrary, it
is a sign of a lack of clear answers.

On the question of meaning, I think it is very likely a mistake to believe that tasks you undertake
are
meaningless simply because an AI could do them better. Most people are not the best in the world at
anything, and it doesn’t seem to bother them particularly much. Of course today they can still
contribute through comparative advantage, and may derive meaning from the economic value they
produce,
but people also greatly enjoy activities that produce no economic value. I spend plenty of time
playing
video games, swimming, walking around outside, and talking to friends, all of which generates zero
economic value. I might spend a day trying to get better at a video game, or faster at biking up a
mountain, and it doesn’t really matter to me that someone somewhere is much better at those things.
In
any case I think meaning comes mostly from human relationships and connection, not from economic
labor.
People do want a sense of accomplishment, even a sense of competition, and in a post-AI world it
will be
perfectly possible to spend years attempting some very difficult task with a complex strategy,
similar
to what people do today when they embark on research projects, try to become Hollywood actors, or
found
companies 28 . The facts that (a)
an AI
somewhere could in principle do this task better, and (b) this task is no longer an economically
rewarded element of a global economy, don’t seem to me to matter very much.

The economic piece actually seems more difficult to me than the meaning piece. By “economic” in this
section I mean the possible problem that most or all humans may not be able to contribute
meaningfully to a sufficiently advanced AI-driven economy. This is a more macro problem than the
separate problem of inequality, especially inequality in access to the new technologies, which I
discussed in Section 3.

First of all, in the short term I agree with arguments that comparative advantage will continue to
keep
humans
relevant and in fact increase their productivity, and may even in some ways level the playing field between
humans. As long as AI is only better at 90% of a given job, the other 10% will cause humans
to
become highly leveraged, increasing compensation and in fact creating a bunch of new human jobs
complementing and amplifying what AI is good at, such that the “10%” expands to continue
to
employ almost everyone. In fact, even if AI can do 100% of things better than humans, but it
remains inefficient or expensive at some tasks, or if the resource inputs to humans and AI’s
are
meaningfully different, then the logic of comparative advantage continues to apply. One area humans
are
likely to maintain a relative (or even absolute) advantage for a significant time is the physical
world.
Thus, I think that the human economy may continue to make sense even a little past the point where
we
reach “a country of geniuses in a datacenter”.

However, I do think in the long run AI will become so broadly effective and so cheap that this will
no
longer apply. At that point our current economic setup will no longer make sense, and there will be
a
need for a broader societal conversation about how the economy should be organized.

While that might sound crazy, the fact is that civilization has successfully navigated major economic
shifts in the past: from hunter-gathering to farming, farming to feudalism, and feudalism to
industrialism. I suspect that some new and stranger thing will be needed, and that it’s something no
one
today has done a good job of envisioning. It could be as simple as a large universal basic income
for
everyone, although I suspect that will only be a small part of a solution. It could be a capitalist
economy of AI systems, which then give out resources (huge amounts of them, since the overall
economic
pie will be gigantic) to humans based on some secondary economy of what the AI systems think makes
sense
to reward in humans (based on some judgment ultimately derived from human values). Perhaps the
economy
runs on Whuffie points. Or perhaps humans will continue to be economically valuable
after all, in some way not anticipated by the usual economic models. All of these solutions have
tons of
possible problems, and it’s not possible to know whether they will make sense without lots of
iteration
and experimentation. And as with some of the other challenges, we will likely have to fight to get a
good outcome here: exploitative or dystopian directions are clearly also possible and have to be
prevented. Much more could be written about these questions and I hope to do so at some later time.

Taking stock

Through the varied topics above, I’ve tried to lay out a vision of a world that is both plausible
if everything goes right with AI, and much better than the world today. I don’t know if this
world is realistic, and even if it is, it will not be achieved without a huge amount of effort and
struggle by many brave and dedicated people. Everyone (including AI companies!) will need to do
their
part both to prevent risks and to fully realize the benefits.

But it is a world worth fighting for. If all of this really does happen over 5 to 10 years—the defeat
of
most diseases, the growth in biological and cognitive freedom, the lifting of billions of people out
of
poverty to share in the new technologies, a renaissance of liberal democracy and human rights—I
suspect
everyone watching it will be surprised by the effect it has on them. I don’t mean the experience of
personally benefiting from all the new technologies, although that will certainly be amazing. I mean
the
experience of watching a long-held set of ideals materialize in front of us all at once. I think
many
will be literally moved to tears by it.

Throughout writing this essay I noticed an interesting tension. In one sense the vision laid out here
is
extremely radical: it is not what almost anyone expects to happen in the next decade, and will
likely
strike many as an absurd fantasy. Some may not even consider it desirable; it embodies values and
political choices that not everyone will agree with. But at the same time there is something
blindingly
obvious—something overdetermined—about it, as if many different attempts to envision a good world
inevitably lead roughly here.

In Iain M. Banks’ The Player of Games 29 , the protagonist—a member of a society called the Culture, which
is
based on principles not unlike those I’ve laid out here—travels to a repressive, militaristic empire
in
which leadership is determined by competition in an intricate battle game. The game, however, is
complex
enough that a player’s strategy within it tends to reflect their own political and philosophical
outlook. The protagonist manages to defeat the emperor in the game, showing that his values (the
Culture’s values) represent a winning strategy even in a game designed by a society based on
ruthless
competition and survival of the fittest. A
well-known post by Scott Alexander has the same thesis—that competition is self-defeating
and
tends to lead to a society based on compassion and cooperation. The “arc of the moral
universe” is another similar concept.

I think the Culture’s values are a winning strategy because they’re the sum of a million small
decisions
that have clear moral force and that tend to pull everyone together onto the same side. Basic human
intuitions of fairness, cooperation, curiosity, and autonomy are hard to argue with, and are
cumulative
in a way that our more destructive impulses often aren’t. It is easy to argue that children
shouldn’t
die of disease if we can prevent it, and easy from there to argue that everyone’s children
deserve that right equally. From there it is not hard to argue that we should all band together and
apply our intellects to achieve this outcome. Few disagree that people should be punished for
attacking
or hurting others unnecessarily, and from there it’s not much of a leap to the idea that punishments
should be consistent and systematic across people. It is similarly intuitive that people should have
autonomy and responsibility over their own lives and choices. These simple intuitions, if taken to
their
logical conclusion, lead eventually to rule of law, democracy, and Enlightenment values. If not
inevitably, then at least as a statistical tendency, this is where humanity was already headed. AI
simply offers an opportunity to get us there more quickly—to make the logic starker and the
destination
clearer.

Nevertheless, it is a thing of transcendent beauty. We have the opportunity to play some small role
in
making it real.

Thanks to Kevin Esvelt, Parag Mallick, Stuart Ritchie, Matt Yglesias, Erik Brynjolfsson, Jim
McClave,
Allan Dafoe, and many people at Anthropic for reviewing drafts of this essay.

To the winners of the 2024 Nobel prize in Chemistry, for showing us all the way.

Footnotes

1 https://allpoetry.com/All-Watched-Over-By-Machines-Of-Loving-Grace
↩

2I do anticipate some minority of people’s reaction will be “this is pretty
tame”.
I think those people need to, in Twitter parlance, “touch grass”. But more importantly,
tame
is good from a societal perspective. I think there’s only so much change people can
handle
at once, and the pace I’m describing is probably close to the limits of what society can
absorb without extreme turbulence. ↩

3I find AGI to be an imprecise term that has gathered a lot of sci-fi baggage
and
hype. I prefer “powerful AI” or “Expert-Level Science and Engineering” which get at what
I
mean without the hype. ↩

4In this essay, I use “intelligence” to refer to a general problem-solving
capability that can be
applied across diverse domains. This includes abilities like reasoning, learning, planning,
and
creativity. While I use “intelligence” as a shorthand throughout this essay, I acknowledge
that the
nature of intelligence is a complex and debated topic in cognitive science and AI research.
Some
researchers argue that intelligence isn’t a single, unified concept but rather a collection
of
separate cognitive abilities. Others contend that there’s a general factor of intelligence
(g
factor) underlying various cognitive skills. That’s a debate for another time. ↩

5This is roughly the current speed of AI systems – for example they can read a
page of text in a couple seconds and write a page of text in maybe 20 seconds, which is
10-100x the speed at which humans can do these things. Over time larger models tend to
make
this slower but more powerful chips tend to make it faster; to date the two effects have
roughly canceled out. ↩

6This might seem like a strawman position, but careful thinkers like Tyler Cowen and Matt Yglesias have raised it as a serious concern (though I
don’t
think they fully hold the view), and I don’t think it is crazy. ↩

7The closest economics work that I’m aware of to tackling this question is
work on
“general purpose technologies” and “intangible
investments” that serve
as complements to general purpose technologies. ↩

8This learning can include temporary, in-context learning, or traditional
training; both will be rate-limited by the physical world. ↩

9In a chaotic system, small errors compound exponentially over time, so that
even
an enormous increase in computing power leads to only a small improvement in how far
ahead
it is possible to predict, and in practice measurement error may degrade this further.
↩

10Another factor is of course that powerful AI itself can potentially be used
to
create even more powerful AI. My assumption is that this might (in fact, probably will)
occur, but that its effect will be smaller than you might imagine, precisely because of
the
“decreasing marginal returns to intelligence” discussed here. In other words, AI will
continue to get smarter quickly, but its effect will eventually be limited by
non-intelligence factors, and analyzing those is what matters most to the speed of
scientific progress outside AI. ↩

11These achievements have been an inspiration to me and perhaps the most
powerful
existing example of AI being used to transform biology. ↩

12“Progress in science depends on new techniques, new discoveries and new
ideas,
probably in that order.” – Sydney Brenner ↩

13Thanks to Parag Mallick for suggesting this point. ↩

14I didn’t want to clog up the text with speculation about what specific
future
discoveries AI-enabled science could make, but here is a brainstorm of some
possibilities:

— Design of better computational tools like AlphaFold and AlphaProteo — that is, a general
AI
system speeding up our ability to make specialized AI computational biology tools.
— More efficient and selective CRISPR.
— More advanced cell therapies.
— Materials science and miniaturization breakthroughs leading to better implanted
devices.
— Better control over stem cells, cell differentiation, and de-differentiation, and a
resulting ability to regrow or reshape tissue.
— Better control over the immune system: turning it on selectively to address cancer and
infectious disease, and turning it off selectively to address autoimmune diseases.

↩

15AI may of course also help with being smarter about choosing what
experiments to
run: improving experimental design, learning more from a first round of experiments so
that
the second round can narrow in on key questions, and so on. ↩

16Thanks to Matthew Yglesias for suggesting this point. ↩

17Fast evolving diseases, like the multidrug resistant strains that essentially
use hospitals as an evolutionary laboratory to continually improve their
resistance
to treatment, could be especially stubborn to deal with, and could be the kind of thing
that
prevents us from getting to 100%. ↩

18Note it may be hard to know that we have doubled the human lifespan within
the
5-10 years. While we might have accomplished it, we may not know it yet within the study
time-frame. ↩

19This is one place where I am willing, despite the obvious biological
differences
between curing diseases and slowing down the aging process itself, to instead look from
a
greater distance at the statistical trend and say “even though the details are
different, I
think human science would probably find a way to continue this trend; after all, smooth
trends in anything complex are necessarily made by adding up very heterogeneous
components.
↩

20As an example, I’m told that an increase in productivity growth per year of
1%
or even 0.5% would be transformative in projections related to these programs. If the
ideas
contemplated in this essay come to pass, productivity gains could be much larger than
this.
↩

21The media loves to portray high
status
psychopaths, but the average psychopath is probably a person with poor economic
prospects and poor impulse control who ends up spending significant time in prison. ↩

22I think this is somewhat analogous to the fact that many, though likely not
all,
of the results we’re learning from interpretability would continue to be relevant even
if
some of the architectural details of our current artificial neural nets, such as the
attention mechanism, were changed or replaced in some way. ↩

23I suspect it is a bit like a classical chaotic system – beset by
irreducible complexity that has to be managed in a mostly decentralized manner.
Though as I say later in this section, more modest interventions may be possible. A
counterargument, made to me by economist Erik Brynjolfsson, is that large companies
(such as
Walmart or Uber) are starting to have enough centralized knowledge to understand
consumers
better than any decentralized process could, perhaps forcing us to revise Hayek’s
insights about who has the best local knowledge. ↩

24Thanks to Kevin Esvelt for suggesting this point. ↩

25For example, cell phones were initially a technology for the rich, but
quickly
became very cheap with year-over-year improvements happening so fast as to obviate any
advantage of buying a “luxury” cell phone, and today most people have phones of similar
quality. ↩

26This is the title of a forthcoming paper from RAND, that lays out roughly
the
strategy I describe. ↩

27When the average person thinks of public institutions, they probably think
of
their experience with the DMV, IRS, medicare, or similar functions. Making these
experiences
more positive than they currently are seems like a powerful way to combat undue
cynicism. ↩

28Indeed, in an AI-powered world, the range of such possible challenges and
projects will be much vaster than it is today. ↩

29I am breaking my own rule not to make this about science fiction, but I’ve
found
it hard not to refer to it at least a bit. The truth is that science fiction is one of
our
only sources of expansive thought experiments about the future; I think it says
something
bad that it’s entangled so heavily with a particular narrow subculture. ↩

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