short analogy essay

What is an Analogy? Explained With 10 Top Examples

What is an analogy? Read our guide with top examples and in-depth explanations so you can wrap your head around this literary device.

Literary devices make your prose more colorful and vivid, allowing the reader to make associations. What is an analogy? An analogy compares two seemingly unlike things to help draw a conclusion by highlighting their similarities. Unlike other comparisons, like similes and metaphors, an analogy gives more detail about the comparison to help the reader understand it better. 

While there are many different types of analogy to study, the best way to understand this and other figures of speech is to consider examples. After reading a few analogies, you will be better equipped to spot them or write your own. And when you have finished here, check out our comparison article, simile vs metaphor .

What is An Analogy?

What are the benefits of using an analogy, analogy examples, 1. a name is a rose from romeo and juliet, 2. life is a shadow from macbeth, 3. the crowd is like a fisherman in “a hanging”, 4. life is like a box of chocolates from forrest gump, 5. pulling out troops is like salted peanuts from henry kissinger, 6. the futility of a new author from cocktail time, 7. the mystery of life in let me count the ways, 8. the push for freedom is like summer’s heat in “i have a dream”, 9. a needle in a haystack, 10. rearranging deck chairs on the titanic, 11. the matrix’s pill analogy, 12. harry potter and the sorcerer’s stone, what is the opposite of an analogy, what is an example of an analogy, what is the simple definition of analogy, what are 5 examples of analogy, what is another word for an analogy.

An analogy compares two concepts, usually to explain or clarify an idea. Writers use analogies to help people understand complex or abstract topics by relating something abstract to the familiar or concrete. They also use them as a type of literary device to improve the readability of their works.

By highlighting similarities, a writer helps readers see how one thing works or behaves by comparing the characteristics of abstract ideas to more familiar ideas. As a result, a concept or idea becomes easier to understand and even more memorable.

For example, a news reporter could employ this word analogy: “The presidential race for 2024 is like a chessboard…” Teachers use different types of analogies to demonstrate a concept to a student. For this reason, analogy tests often form part of standardized tests in any good English curriculum.

Analogies work in the real world too! For example, if a running coach wants to explain how a runner can run faster, they could use an analogy like “Pump your arms like a train” to help people understand how they should use their arms and legs to run faster. You might also be interested in learning  what is tautology .

Examples of analogies exist in classic literature, the latest books, movies and TV shows. Here are a few:

Often, analogies compare abstract concepts to something you can touch and feel. There are several examples of analogy in William Shakespeare’s Romeo and Juliet. In this analogy, the playwright compares someone’s name to a rose. The rose retains its sweet smell no matter how it is named, as does the person, regardless of his name. Read our guide to the best books of classic literature .

“If you want my final opinion on the mystery of life and all that, I can give it to you in a nutshell. The universe is like a safe to which there is a combination. But the combination is locked up in the safe.”

Life is a difficult concept to understand, making it a favorite topic for people who write analogies. In Act V of Macbeth, Shakespeare creates an analogy example by comparing a person’s life, and its brevity, to a fleeting shadow:

“Life’s but a walking shadow, a poor player That struts and frets his hour upon the stage And then is heard no more. It is a tale  Told by an idiot, full of sound and fury, Signifying nothing.”

Because life is so fleeting, this analogy works. The reader can see the shadow flitting about on the stage, then disappearing, reminding the reader how short life really is. You might also find these  headings and subheadings examples  helpful.

Some analogies take a little more time to explain yet still compare unlike things to make a point. For example, in his essay entitled  A Hanging  George Orwell describes the crowd gripping a man as they lead him to the gallows. The analogy is the comparison to the way a man would hold a slippery fish:

“They crowded very close about him, with their hands always on him in a careful, caressing grip, as though all the while feeling him to make sure he was there. It was like men handling a fish which is still alive and may jump back into the water. But he stood quite unresisting, yielding his arms limply to the ropes, as though he hardly noticed what was happening.”

This analogy is also an example of a simile because it uses the word “like” to make the comparison. However, because it extends beyond just one statement but has a complete description and explanation, it brings more imagery to the reader’s mind and thus is an analogy. Read our guide to the  best satirical authors .

Some analogies are short and sweet, rather than taking up an entire literary work. In the movie Forrest Gump, both the title character and his mother refer to life as a “box of chocolates.” In one of the most famous figures of speech from this movie, Forest says:

“My mom always said life was like a box of chocolates. You never know what you’re gonna get.”

Though this is a simple statement, it is an example of an analogy. The reader has probably experienced the feeling of grabbing chocolate and wondering what flavor it is, so this is a good analogy. But, like life, that box of chocolates always has the potential to give you the unexpected. You might also be wondering,  what is point of view?

Though technically a historian and not a literary genius, Henry Kissinger was famous for many of his analogies. One of his most commonly quoted is this:

“Withdrawal of U.S. troops will become like salted peanuts to the American public; the more U.S. troops come home, the more will be demanded. This could eventually result, in effect, in demands for unilateral withdrawal.”

This quote comes from a  memorandum Kissinger sent to President Nixon  regarding the conflict in Vietnam. He warned the president that bringing troops home a little at a time would create demand for more withdrawal, just like eating tasty peanuts makes you want to eat more. 

Writing a book is definitely challenging, especially when doing so for the first time. This fact is the source of one famous analogy in literature. In  Cocktail Time , P.G. Wodehouse compares a new author to someone performing an impossible task:

“It has been well said that an author who expects results from a first novel is in a position similar to that of a man who drops a rose petal down the Grand Canyon of Arizona and listens for the echo.”

Clearly, expecting to hear an echo from a rose petal at the Grand Canyon is foolishness. Thus, based on this analogy, the logical argument that expecting to see significant returns from a first novel is also foolish. You might also be wondering  what is a split infinitive .

In his novel  Let Me Count the Ways , Dutch author and journalist  Peter De Vries  compares life and a safe. He writes:

In this analogy, the safe can’t be unlocked. Similarly, the mystery of life is something people can’t fully understand.

Speechwriters who are good at their jobs often use analogies to make their words more memorable. In his famous speech, “I Have a Dream,” Martin Luther King, Jr., makes an analogy between the anger of African-Americans and the heat of summer in this quote:

“This sweltering summer of the Negro’s legitimate discontent will not pass until there is an invigorating autumn of freedom and equality.”

Just like the heat of summer is unquenchable, the frustration of those facing endless prejudice cannot be quenched. Yet when freedom comes, it is like the relief of the cool autumn breeze. This quote is still used today when people remember the famous civil rights activist.

Finding a needle in a haystack is a nearly impossible task. This catchphrase or analogy example is often applied to tasks that seem out of reach. For instance, one common analogy says:

“Finding a good man is as easy as finding a needle in a haystack.”

This analogy indicates it is nearly impossible to find a “good man.” Though unfair to the male gender, it does make its point through the use of analogy. Most people can picture digging through the hay to find a needle, but to no avail, which makes the analogy work.

This analogy does not come from any famous literary work or speech but from a well-known historical moment. The sinking of the Titanic was one such event. Sometimes people, when talking about something futile, will say:

“That’s as useful as rearranging deck chairs on the Titanic.”

Since the Titanic was a doomed vessel, the futility of the effort is seen in this use of figurative language. The phrase can apply to any effort that would not matter because the result is a failure, like the sinking of the infamous ship. Check out our metonymy examples .

In The Matrix , there is a famous scene where Morpheus presents the red pill/blue pill analogy to Neo. The analogy is a turning point in the movie where Neo has to pick which path he wants to go down. The red pill represents embracing the uncomfortable truth and becoming aware of the real world he lives in. The blue pill represents choosing the familiar and comfortable path where he can remain in his world, oblivious to the dark reality he suspects.

“You take the blue pill, the story ends. You wake up in your bed and believe whatever you want to believe. You take the red pill, you stay in Wonderland, and I show you how deep the rabbit hole goes.”

J.K. Rowling uses analogies throughout her works, often to give insight into the minds and personalities of the characters. In Harry Potter and the Sorcerer’s Stone , Professor Dumbledore speaks to Harry and imparts some of his famous wisdom.

“It does not do to dwell on dreams and forget to live.”

In this analogy example sentence, he suggests that while having dreams and aspirations are important, it’s just as important to be grounded and present in the current moment. The analogy aims to show Harry that he should balance his ambition and reality and become mindful in the midst of the chaos that he lives in. It also encourages Harry to let go of regrets and become fully present in his life as it is today.

An antithesis highlights the differences between two contrasting ideas. For example, the analogy “Man plans, and God laughs” shows how we can strive and work towards a goal, only for God or fate to intervene and uproot our best plans. For further reading on a similar subject, check out our post on examples of metaphors in literature .

FAQs About What is an Analogy

An example of an analogy is “Hope is the lighthouse that stands tall amidst the stormy seas of despair.” The analogy emphasizes the idea that hope can help us navigate through the storms of life, guiding us toward a better future and helping us persevere in the face of challenges.

An analogy is a comparison between two things that are alike in some way, often used to help explain something or make it easier to understand.

1. Her laughter was music to his ears. 2. Time is money. 3. He is a shining star in the world of science. 4. The classroom was a zoo during the group activity. 5. Life is a journey with its share of twists and turns.

A related term for analogy is comparison. A comparison is a way of describing the similarities or differences between two things in order to better understand them.

Meet Rachael, the editor at Become a Writer Today. With years of experience in the field, she is passionate about language and dedicated to producing high-quality content that engages and informs readers. When she's not editing or writing, you can find her exploring the great outdoors, finding inspiration for her next project.

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• Literary Terms

When & How to write an Analogy

• Definition & Examples
• When & How to write an Analogy

How to Write an Analogy

You should use analogies in your writing when you want to show strong support by comparison. Here are some examples of how to use them:

Normal Sentence:

He ran incredibly fast in the race.

With Analogy:

In the race, he ran with the grace and speed of a cheetah—smooth, flawless, and natural, as if he had been raised running across the plains of Africa.

Those two are very close.

Those two unlikely friends are surprisingly close, like a shark and its cleaner fish—though they have different qualities and purposes, it is clear that neither could survive without the

Although analogies are useful and essential devices, they can be surprisingly difficult to use effectively! You don’t want to make comparisons to just anything, or your writing may start to look sloppy and careless. Here are some examples of poor analogies to show you the kinds of common mistakes you should try to avoid:

Poor Analogy : He ran as fast as a cheetah in the race.

Why It’s Poor : Wait, there was a cheetah in the race? No, of course not. That phrase is a dangling modifier . So just move it to the beginning, as in the sentence above (“In the race, he ran…”).

Poor Analogy : On that warm summer day, we went down to the beach, where the sand was as white as snow.

Why It’s Poor : The author has done so much to show the reader that the setting is a warm, sunny beach in summer. But the word “snow” completely undermines that by bringing up images of cold, grey winter. Rather than improving  the imagery, the analogy actually works against it.

When to Use Analogy

Analogies can be an extremely powerful addition to your writing, so experiment! Using analogies is a really useful skill for improving your powers of logic, reasoning, and writing, and the best way to learn it is to practice.

When you experiment with analogies in your writing, keep the following principles in mind:

• Make sure it’s clear what aspect(s) of the two objects you want to compare.
• Draw an analogy to something concrete , ideally something that people can actually visualize in their minds. If you’re trying to explain an abstract idea, it doesn’t help to compare it to another abstract idea, but it might help a lot if you compare it to something tangible!
• If you’re using analogies in creative writing, make sure they’re suited to the setting ! If the story is set on a boat, try to use analogies having to do with water or islands. Remember the example with the sand and the snow. In that case, the problem was that the setting was all wrong – snow doesn’t belong on a warm, sandy beach!

List of Terms

• Alliteration
• Amplification
• Anachronism
• Anthropomorphism
• Antonomasia
• APA Citation
• Aposiopesis
• Autobiography
• Bildungsroman
• Characterization
• Circumlocution
• Cliffhanger
• Comic Relief
• Connotation
• Deus ex machina
• Deuteragonist
• Doppelganger
• Double Entendre
• Dramatic irony
• Equivocation
• Extended Metaphor
• Figures of Speech
• Flash-forward
• Foreshadowing
• Intertextuality
• Juxtaposition
• Literary Device
• Malapropism
• Onomatopoeia
• Parallelism
• Pathetic Fallacy
• Personification
• Point of View
• Polysyndeton
• Protagonist
• Red Herring
• Rhetorical Device
• Rhetorical Question
• Science Fiction
• Self-Fulfilling Prophecy
• Synesthesia
• Turning Point
• Understatement
• Urban Legend
• Verisimilitude
• Essay Guide
• Cite This Website

30 Writing Topics: Analogy

Ideas for a Paragraph, Essay, or Speech Developed With Analogies

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• An Introduction to Punctuation
• Ph.D., Rhetoric and English, University of Georgia
• M.A., Modern English and American Literature, University of Leicester
• B.A., English, State University of New York

An analogy is a kind of comparison that explains the unknown in terms of the known, the unfamiliar in terms of the familiar.

A good analogy can help your readers understand a complicated subject or view a common experience in a new way. Analogies can be used with other methods of development to explain a process , define a concept, narrate an event, or describe a person or place.

Analogy isn't a single form of writing. Rather, it's a tool for thinking about a subject, as these brief examples demonstrate:

• "Do you ever feel that getting up in the morning is like pulling yourself out of quicksand? . . ." (Jean Betschart, In Control , 2001)
• "Sailing a ship through a storm is . . . a good analogy for the conditions inside an organization during turbulent times, since not only will there be the external turbulence to deal with, but internal turbulence as well . . ." (Peter Lorange, Leading in Turbulent Times , 2010)
• "For some people, reading a good book is like a Calgon bubble bath — it takes you away. . . ." (Kris Carr, Crazy Sexy Cancer Survivor , 2008)
• "Ants are so much like human beings as to be an embarrassment. They farm fungi, raise aphids as livestock, launch armies into wars, use chemical sprays to alarm and confuse enemies, capture slaves. . . ." (Lewis Thomas, "On Societies as Organisms," 1971)
• "To me, patching up a heart that'd had an attack was like changing out bald tires. They were worn and tired, just like an attack made the heart, but you couldn't just switch out one heart for another. . . ." (C. E. Murphy, Coyote Dreams , 2007)
• "Falling in love is like waking up with a cold — or more fittingly, like waking up with a fever. . . ." (William B. Irvine, On Desire , 2006)

British author Dorothy Sayers observed that analogous thinking is a key aspect of the writing process . A composition professor explains:

Analogy illustrates easily and to almost everyone how an "event" can become an "experience" through the adoption of what Miss [Dorothy] Sayers called an "as if" attitude. That is, by arbitrarily looking at an event in several different ways, "as if" if it were this sort of thing, a student can actually experience transformation from the inside. . . . The analogy functions both as a focus and a catalyst for "conversion" of event into experience. It also provides, in some instances not merely the To discover original analogies that can be explored in a paragraph , essay, or speech, apply the "as if" attitude to any one of the 30 topics listed below. In each case, ask yourself, "What is it like ?"

Thirty Topic Suggestions: Analogy

• Working at a fast-food restaurant
• Moving to a new neighborhood
• Starting a new job
• Quitting a job
• Watching an exciting movie
• Reading a good book
• Going into debt
• Getting out of debt
• Losing a close friend
• Leaving home for the first time
• Taking a difficult exam
• Making a speech
• Learning a new skill
• Gaining a new friend
• Responding to bad news
• Responding to good news
• Attending a new place of worship
• Dealing with success
• Dealing with failure
• Being in a car accident
• Falling in love
• Getting married
• Falling out of love
• Experiencing grief
• Experiencing joy
• Overcoming an addiction to drugs
• Watching a friend destroy himself (or herself)
• Getting up in the morning
• Resisting peer pressure
• Discovering a major in college
• The Value of Analogies in Writing and Speech
• Understanding Analogy
• 30 Writing Topics: Persuasion
• Learn How to Use Extended Definitions in Essays and Speeches
• Development in Composition: Building an Essay
• 501 Topic Suggestions for Writing Essays and Speeches
• Topic In Composition and Speech
• Definition and Examples of Transitional Paragraphs
• List of Topics for How-to Essays
• How to Structure an Essay
• How to Write a Narrative Essay or Speech
• The Ultimate Guide to the 5-Paragraph Essay
• Conclusion in Compositions
• How to Write a Great Essay for the TOEFL or TOEIC
• Understanding Organization in Composition and Speech
• Personal Essay Topics

The word analogy has its origins in Greek analogikótita, meaning proportionality. In ancient times, analogies were used to things by showing how they were related, usually in philosophical arguments. When the Greeks used analogy, they would often compare two sets of words side-by-side to illustrate this relationship. Example: white is to black as on is to off, meaning that black and white are complete opposites.

What is Analogy?

An analogy compares things by showing how they are alike. The comparison is often used to make a point or better describe something. Analogies so more than compare. They show and explain . Analogies are often confused with similes and metaphors. However, they are not the same thing. While similes and metaphors may be used in an analogy, an analogy does more than compare – it explains. It is good to remember:  Similes and Metaphors can be used in Analogy , but not all Analogies are similes or metaphors .

How to pronounce Analogy?

When do writers use analogy.

Writers should use analogy when they want to give readers a better understanding of the abstract or complex. By using an analogy writers make these concepts easier to understand. use Analogy literary device to compare two different things or ideas to explain a concept or make a point. Analogy is often used to simplify an idea or explanation.

A favorite example of analogy is this: “Rearranging those chairs is about as useful as rearranging the chairs on the Titanic. In this instance, the analogy is being used to point out that no matter how many chairs are rearranged doesn’t matter; it’s a futile effort.

Some Tips for Using Analogy in Your Writing

If you wish to work analogy into your writing, there are of course a few tips to follow to achieve the desired effect.

• Think of ways to inspire .
• Think about your audience and use comparisons they will understand.
• Create simple, easy to understand imagery .
• Work to compare and contrast .

And Remember : Analogy not only compares, it shows and explains .

The Two Types of Analogy

There are two types of analogy. These types are:

• Analogies which identify shared relationships – This type of analogy is often found in logical arguments and compares things that are technically unrelated. When using this type of analogy, comparisons are straightforward and generally made in sets. Example: “White is to black like on is to off,” meaning that black and white are total opposites.
• Analogies which identify shared abstractness – Analogies of this type involve two things that are technically unrelated but share similar characteristics and are useful for making your audience understand abstract concepts. For example, “Raising a child is like gardening, it takes both patience and practice.” Since parenting is a complex, abstract concept, this analogy helps to explain that like gardening, you must tend to your children with patience so that they may grow to be strong.

Analogy in Literature 📚

The House in Paris , Elizabeth Bowen uses analogy to say that like the saucer supports the cup, our memories keep love alive.

“Memory is to love what the sauce’r is to cup.”

Another modern example of analogy in literature comes from Peter de Vries in Let Me Count the Ways :

“If you want my final opinion on the mystery of life and all that, I can give it to you in a nutshell. The universe is like a safe to which there is a combination. But the combination is locked up in the safe.”

In this passage, de Vries uses the analogy that like the combination to the safe, life is a mystery, meaning that just as we may never understand the meaning of life, the safe may never be unlocked.

And finally, you cannot have a discussion about literary devices without including Shakespeare . After all, he seems to be a master of them all. In MacBeth , Act V, he compares life to a passing shadow – it is fleeting and comes as easily as it goes.

“Life’s but a walking shadow, a poor player

That struts and frets his hour upon the stage

And then is heard no more. It is a tale

Told by an idiot, full of sound and fury,

Signifying nothing.”

And in Poetry ✍🏽

When examining analogy in poetry, the task can become. Analogies can be harder to identify because in shorter poems, you may find the analogy is not contained to a single line or two but rather, the entire poem. While that’s not the case in our first example, we have included one such example for review.

“T here is no Frigate Like a Book ,” Emily Dickinson – 

“ There is no Frigate like a Book

To take us Lands away

Nor any Coursers like a Page

Of prancing Poetry –

This Traverse may the poorest take

Without oppress of Toll –

How frugal is the Chariot That bears the Human Soul –”

In the poem above, the sections of interest have been highlighted to further explain the analogy in the line (and title), “There is no frigate like a book.” In this line, Dickinson compares a book to a war ship. The abstract concepts Dickinson refers to in this analogy are that of the imagination and the soul. She is saying that a book, like a warship, possesses immense power but also, it has the ability to take the reader all over the world if they can just imagine it.

This example is a little more complex, in that as previously noted,  the entire poem is the analogy. However, what Frost wants to convey is that as the seasons change, life also changes with each passing day. In “ Nothing Gold Can Stay, ” Robert Frost uses analogy to compare seasons to life. He writes:

“Nature’s first green is gold,

Her hardest hue to hold.

Her early leaf’s a flower;

But only so an hour.

Then leaf subsides to leaf.

So Eden sank to grief,

So dawn goes down to day.

Nothing gold can stay.”

As the discussion moves forward, this section ends with William Wadsworth Longfellow and his poem, “ The Day is Done .”

“The day is done, and the darkness

Falls from the wings of Night ,

As a feather is wafted downward

From an eagle in his flight.”

In Longfellow’s analogy, he compares the coming of night to a feather falling gently and peacefully from an eagle’s wing.

Analogy in Film and Dialogue 🎥

“Life is like a box of chocolates, you never know what you’re gonna get” – Forrest Gump (1994)

In this clip, Forrest compares life to the unpredictability of a box of chocolates. What the writers of this scene wished to convey is that just as you never know what you’ll get in a box of assorted chocolates, life is equally unpredictable.

Analogy in Advertising 📺

Today, one of the most effective ad campaigns uses people as analogy . This was brought to life in the recent Apple commercial featuring Justin Long as a Mac.

But when it comes to marketing, the analogy itself can become abstract as in the example above. Analogies can be presented as images as in the Amazon shopping logo featuring the shopping cart and the A to Z connected with a smile. More traditional examples include ads such as the ad slogan, “ Like a good neighbor, State Farm is there .” The comparison being made is that of a good neighbor and the insurance company. What means a good neighbor is always there in a time of need and like that neighbor the insurance company will be there ready and waiting when you need it.

Often Mistaken for .. 👥

• Simile – A comparison between two unrelated things using the word “like” or “as.” Example: “The biscuit is as salty as a pickle.”
• Metaphor – A figure of speech describing an action or object in a way that is not literally true. Example: “Bob is a couch potato.”

What is an analogy in literature?

An analogy is a literary device that establishes a relationship based on similarities between two concepts or ideas. By conveying an idea or an argument with the help of an analogous situation, it makes it easier to understand a new idea by comparing it to a familiar one.

How does an analogy differ from a metaphor and a simile?

While analogies, metaphors, and similes all compare two different things, analogies are used for clarification, explanation, or argumentation, showing how two things are alike in more than one aspect. Similes make a comparison using “like” or “as,” and metaphors do so by stating something is something else, often in a more poetic manner.

Why are analogies important in literature?

Analogies are important because they help clarify complex or unfamiliar concepts by comparing them to something more familiar, making the new information easier to grasp. They also enhance the reader’s engagement by encouraging them to make connections between different ideas or themes.

Can you give examples of how analogies are used?

Examples of analogies include comparing the structure of an atom to a solar system to explain electron orbits, or likening the mind to a computer when discussing human memory. These comparisons help clarify the less familiar concept by drawing parallels to something understood.

How can I identify an analogy in a text?

To identify an analogy, look for a comparison that is used to explain, clarify, or argue for a concept through its similarities with another, more familiar concept. Analogies often go beyond simple comparisons to explore the relationships between different aspects of the two subjects being compared.

Mastering the Art of Analogies: Examples and Tips for Nonfiction Writers

by Harry Wallett

In this blog post, we’ll explore the ins and outs of using analogies to create vivid, memorable, and easy-to-understand pieces that resonate with your readers.

Picture this: you’re trying to explain a complex idea or concept to your readers, and you can see their eyes glazing over as they struggle to grasp your point. Frustrating, isn’t it? That’s where analogies come in.

By comparing your complicated idea to something more familiar and relatable, you can help your readers understand and retain the information you’re sharing.

In fact, analogies are an incredibly valuable addition to your writer’s toolbox. To help you master this technique, we’ll cover the basics of what an analogy is and how it differs from metaphors and similes.

We’ll then delve into the different types of analogies and provide examples from various nonfiction sources to show you how it’s done.

Let’s get started!

What is an analogy?

An analogy is a comparison between two things that are alike in some way, usually to explain a complex idea or concept in simpler terms.

Unlike metaphors and similes, which compare two things directly, analogies highlight the relationship between the two things being compared.

For example, you could say that writing is like cooking: both require skill, creativity, and the right ingredients (words or ideas) to create a satisfying result.

Types of Analogies

Understanding the different types of analogies can help you use them more effectively in your nonfiction writing. By recognizing the various forms that analogies can take, you’ll be better equipped to select the right type of analogy for your specific writing needs.

Here, we’ll explore the three main types of analogies commonly used in nonfiction writing:

Structural Analogies

Structural analogies focus on the similarities in the structure or organization of the two things being compared.

These analogies help to draw parallels between the arrangement or composition of different entities, making it easier for your readers to understand the underlying structure of a complex system.

For example, you might compare the hierarchy of a company to a military chain of command. In this analogy, the CEO is like a general, middle managers are like officers, and frontline employees are like enlisted soldiers.

This comparison can help your readers visualize the organizational structure of a company and understand the relationships between different levels of management.

Functional Analogies

Functional analogies highlight the similarities in how two things work or perform. These types of analogies help to explain the purpose or function of something by comparing it to another object or system with a similar function.

For instance, you could compare the human heart to a pump, as both have the primary function of moving fluid through a system. The heart pumps blood throughout the body, while a mechanical pump might move water through pipes.

By drawing this parallel, your readers can better understand the role of the heart in the circulatory system.

Conceptual Analogies

Conceptual analogies explore the similarities in the underlying concepts or ideas of the two things being compared.

These analogies can be particularly useful for explaining abstract ideas or theories by connecting them to more concrete or familiar concepts.

An example might be comparing the internet to a library, as both serve as repositories of information.

While the internet is a vast digital network that connects users to websites, articles, and other resources, a library is a physical space housing books, journals, and other sources of knowledge.

This analogy can help your readers understand the broader concept of the internet as a massive, interconnected storehouse of information.

How to Create Effective Analogies

Crafting the perfect analogy can be a bit of an art form, but with practice and a few guiding principles, you can create powerful comparisons that will elevate your nonfiction writing.

Here are some key tips to keep in mind when creating effective analogies:

Choose relatable and easily understood comparisons

The success of an analogy often hinges on its relatability. To help your readers grasp a complex concept, choose comparisons that are familiar and easily understood.

When your analogy is based on common experiences or objects, it becomes more accessible, allowing your readers to quickly make connections between the two ideas.

Consider your target audience’s background, interests, and experiences when selecting a comparison, and opt for analogies that will resonate with them.

Ensure the analogy supports your main point

An effective analogy should serve to clarify your argument or idea, not distract from it. Be sure that your chosen comparison supports your main point and enhances your readers’ understanding of the topic.

If an analogy seems to muddy the waters or lead your readers away from your central message, it’s best to rethink your approach and choose a different comparison that better aligns with your goals.

Be concise and avoid over-complicating the analogy

While it can be tempting to get lost in the details of a comparison, remember that the goal of an analogy is to simplify a complex concept for your readers. Aim to be concise and avoid over-complicating the analogy, as this can lead to confusion rather than clarification.

Focus on the most relevant and impactful similarities between the two ideas, and leave out extraneous details that might detract from your main point.

Test your analogy for effectiveness

Before committing to an analogy in your writing, it’s a good idea to test it for effectiveness. Consider running your analogy by a trusted friend, colleague, or editor, and ask for their feedback.

Does the analogy help them understand the concept better? Is it clear and concise? Their input can be invaluable in determining whether your analogy is hitting the mark or if it needs further refinement.

Don’t overuse analogies

While analogies can be powerful tools in nonfiction writing, it’s important not to overuse them. Relying too heavily on analogies can make your writing feel repetitive or overly simplistic.

Strike a balance by using analogies judiciously, reserving them for instances where they truly enhance your readers’ understanding of a complex idea or concept.

Examples of Analogies in Nonfiction Writing

Let’s take a look at some examples:

• In his book “The Tipping Point,” Malcolm Gladwell compares the spread of ideas and trends to the spread of a virus, highlighting how certain factors can cause ideas to “infect” large numbers of people quickly and seemingly without warning.
• In an article on climate change, you might use the analogy of a bathtub filling with water to explain how greenhouse gas emissions accumulate in the atmosphere, leading to global warming.
• Martin Luther King Jr., in his famous “I Have a Dream” speech, used the analogy of a “bad check” to represent the unfulfilled promises of justice and equality for African Americans in the United States.

Tips for using analogies in your writing

Here are three handy tips for incorporating analogies into your nonfiction writing:

Know your audience: Make sure your analogies are appropriate for your target readers and consider their background knowledge and experiences.

Use analogies sparingly and intentionally: While they can be powerful tools, overusing analogies can make your writing feel cluttered or forced. Use them when they genuinely enhance your message.

Revise and refine your analogies for clarity and impact: Don’t be afraid to tweak or even scrap an analogy if it’s not working as well as you’d like. Sometimes it takes a bit of trial and error to find the perfect comparison.

Benefits of using analogies in nonfiction writing

So, why should you bother incorporating analogies into your writing? There’s a multitude of reasons, and the benefits are too good to ignore.

Let’s dive into some of the key advantages of using analogies in your nonfiction work:

They enhance understanding and retention of complex concepts

Analogies are a fantastic way to break down complicated ideas into more digestible chunks. By comparing complex concepts to familiar and relatable experiences or objects, you make it easier for your readers to understand and remember your message.

This can be especially helpful when you’re tackling subjects that may be unfamiliar or difficult for your audience to grasp, like scientific or technical concepts.

They engage your reader’s imagination and emotions

By drawing on familiar experiences or images, you create vivid mental pictures that can capture your reader’s attention and make your writing more engaging and memorable.

This emotional connection can also help your readers to empathize with the subjects of your writing, fostering a deeper understanding and appreciation for your topic.

They strengthen your argument and persuade your reader

Analogies can serve as powerful rhetorical devices, helping to drive home your point and convince your reader of your perspective. When used effectively, an analogy can clarify your argument, making it more accessible and persuasive.

This is particularly useful when you’re trying to explain an abstract concept or idea, as it helps to ground your argument in more concrete terms that your readers can easily understand and relate to.

They can aid in creative problem-solving and critical thinking

Analogies can also help your readers see connections between seemingly unrelated concepts or ideas, fostering creative problem-solving and critical thinking.

By exploring similarities between different subjects, you can help your readers develop new perspectives and insights, broadening their understanding of the world around them.

This can lead to novel approaches to tackling challenges, as well as a deeper appreciation for the interconnectedness of various aspects of our lives.

They create a relatable and personable writing style

Incorporating analogies into your writing can also make your work feel more relatable and personable. This can be particularly beneficial in nonfiction writing, where the subject matter might be dry or technical.

By using analogies that resonate with your readers’ experiences or interests, you can create a more engaging and approachable tone, making your writing feel more like a conversation than a lecture.

Wrapping Things Up

Mastering the art of using analogies in your nonfiction writing can transform your work from good to great. By thoughtfully incorporating analogies, you’ll be able to break down complex concepts, engage your readers on a deeper level, and create memorable connections that will make your writing stand out in a sea of content.

But, as with any writing technique, the key to truly mastering analogies lies in practice and experimentation. Don’t be afraid to play around with different comparisons and ideas, and remember that finding the perfect analogy may take some time and effort.

Keep refining your approach, and you’ll soon discover that the power of analogies lies not only in their ability to explain and persuade but also in their capacity to reveal new insights and perspectives that can enrich both your writing and your readers’ understanding of the world.

Happy writing!

Harry Wallett is the Managing Director of Cascadia Author Services. He has a decade of experience as the Founder and Managing Director of Relay Publishing, which has sold over 3 million copies of books in all genres for its authors, and looks after a team of 50+ industry professionals working across the world.

Harry is inspired by the process of book creation and is passionate about the stories and characters behind the prose. He loves working with the writers and has shepherded 1000s of titles to publication over the years. He knows first-hand what it takes to not only create an unputdownable book, but also how to get it into the hands of the right readers for success.

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What Is Analogy? Definition, Usage, and Literary Examples

Analogy definition.

An  analogy  (uh-NAHL-uh-gee) is a rhetorical device in which a writer compares the shared qualities of two unrelated objects. They are different from  similes  and  metaphors , which also compare unrelated objects by equating them. However, an analogy can employ either one to drive home its larger point. Analogies support logic, present rational arguments, and back up ideas by showing the relationship between disparate things.

The word  analogy  comes from the Greek  analogia , meaning “proportion,” which builds off  ana , meaning “according to,” and  logos , meaning “ratio.”

How to Construct an Analogy

Most analogies in literature,  rhetoric , and everyday communication contain two components: the unknown concept, which is the target, and the known concept, which is the source. The target is the idea the analogy hopes to explain, while the source is the idea used to explain it. The source is something familiar or widely understood to most people; the target is something unfamiliar and mysterious.

When creating a link between the two concepts, writers are essentially making the unfamiliar into something familiar. For example, take the classic line from  Forrest Gump : “Life is like a box of chocolates. You never known what you’re gonna get.” Forrest, quoting his mother in this line, uses a box of assorted chocolates as the source, comparing it to a target that is nebulous and difficult to understand: life. Connecting the two concepts illuminates a specific insight about the randomness of life.

In logic and reasoning, and occasionally in literature, analogies are a four-part comparison expressed via the formula of A:B::C:D, or A is to B as C is to D. This comparison depends on the relationship between A and B and the relationship between C and D to make its point, so A can never be D, and B can never be C. For instance, in the analogy “Haggis is to Scotland as caviar is to Russia,” haggis (A) is a food associated with Scotland (B), just as caviar (C) is a food associated with Russia (D). It explains that haggis originated in Scotland by equating its relationship to the relationship between caviar and Russia, as the former originated in the latter.

Relationships that Analogies Can Convey

There are several different comparative concepts that can fit into the A:B::C:D formula.

• Opposite relationships, or antonyms: “cold is to hot as night is to day”
• Similar relationships, or synonyms: “draw is to sketch as sofa is to couch”
• Cause and effect relationships: “smiles are to joy as tears are to grief”
• Part-to-whole relationships: “finger is to hand as leaf is to tree”
• Location relationships: “apples are to orchards as fish are to sea”
• Object-to-action relationships, wherein objects are paired with associated actions: “bake is to pie as simmer is to soup”
• Performer-to-action relationships: “actor is to acting as writer is to writing”
• Performer-to-object relationships: “plumber is to wrench as artist is to paintbrush”
• Function relationships: “pencil is to writing as knife is to cutting”
• Attribute or characteristic relationships: “teachers emit wisdom as lamps emit light”
• Classification relationships: “ waltz is to dance as American Beauty is to rose”

The Function of Analogies

An analogy helps make an abstract concept more tangible and relatable. Many professionals rely on sharing information, and analogies play an important role in making that information understandable. Writers, teachers, advertising and marketing professionals, government officials, scientists, and healthcare providers are just a few of the occupations that involve disseminating information to the general public. Employing analogies is a common method of ensuring an audience understands what they hear.

Analogies also inject substance and emotion into an idea or image. Writers mainly utilize this function to convey meaning and beauty in the stories they tell. It’s nearly impossible to read a novel or a  poem  without finding at least one analogy.

Finally, analogies make compelling arguments in rhetoric. Advertising and marketing lingo, political debates, and  didactic  nonfiction works are some of the arenas where analogies present powerful, persuasive arguments. In 2009, President Barack Obama responded to the Republican criticism of his proposals with a potent analogy comparing politicians’ responsibilities with mopping up messes. “I’m busy. Nancy’s busy with our mops cleaning up somebody else’s mess,” he said. “We don’t want somebody sitting back saying ‘You’re not holding the mop the right way.’ Why don’t you grab a mop? Why don’t you help clean up? ‘You’re not mopping fast enough! That’s a socialist mop!’ Grab a mop. Let’s get to work.”

Analogies, Similes, and Metaphors

While these terms all involve making comparisons, they differ in that analogies merely point out commonalities between two unrelated things, while  similes  and  metaphors  are  figures of speech  that imply the unrelated things are equals. Both similes and metaphors are popular in the target/source approach to analogies.

The difference between similes and analogies is subtle. A simile compares two things through the words  like  or  as . While it can have a powerful effect when making comparisons, analogies address more detailed explanations that elevate the relationship between the compared concepts. The earlier  Forrest Gump  quote is an example of both a simile and an analogy. The first part of the movie line—“Life is like a box of chocolates”—is a simile. The subsequent explanation—“You never know what you’re gonna get”—expands upon the simile’s concept to make a larger point; thus, it is an analogy.

Metaphors and analogies have a similar relationship. Metaphors compare two objects directly, without the linking words that similes use. For example, here is a famous excerpt from the  William Shakespeare  classic  Romeo and Juliet , spoken by Juliet:

What’s in a name? That which we call a rose

By any other word would smell as sweet.

So Romeo would, were he not Romeo call’d,

Retain that dear perfection which he owes

Without that title.

In this passage, Juliet compares Romeo’s perceived perfection to a rose’s sweet scent; since she does not use linking words to state this similarity, her description is a metaphor. It becomes an analogy because she expounds upon it. She starts by declaring that names are irrelevant. To prove this point, she posits that a rose will always smell like a rose no matter what one might call it. Bringing the analogy to a close, she says that, just like the rose, Romeo will remain who he is—someone she loves—no matter what name he has.

Examples of Analogies in Literature

1. William Shakespeare,   As You Like It

Shakespeare’s comedy involves a woman named Rosalind escaping persecution at her uncle’s court and fleeing to the Forest of Arden. There, she finds a cast of quirky characters, including an introspective traveler named Jacques. He delivers one of Shakespeare’s most memorable monologues:

All the world’s a stage,

And all the men and women merely players;

They have their exits and their entrances,

And one man in his time plays many parts,

His acts being seven ages. At first, the infant,

Mewling and puking in the nurse’s arms.

Then the whining schoolboy, with his satchel

And shining morning face, creeping like snail

Unwillingly to school. And then the lover,

Sighing like furnace, with a woeful ballad

Made to his mistress’ eyebrow. Then a soldier,

Full of strange oaths and bearded like the pard,

Jealous in honor, sudden and quick in quarrel…

In this passage, Jacques likens the world to a stage and all the world’s inhabitants to actors performing on the stage. By saying “one man in his time plays many parts,” Jacques—and Shakespeare through him—implies that the roles people fulfill evolve throughout the natural span of human life. Even further, he compares this evolution to the “acts” that make up a play.

2. T.S. Eliot, “The Love Song of J. Alfred Prufrock”

Eliot’s  narrative poem  encompasses a series of thoughts by a narrator on the search for love in a loveless world. Despite the title, it is less a love song and more of a collection of fragmented ideas about frustrated and unexpressed love and devotion.

Let us go then, you and I,

When the evening is spread out against the sky

Like a patient etherized upon a table;

Let us go, through certain half-deserted streets,

The muttering retreats

Of restless nights in one-night cheap hotels

And sawdust restaurants with oyster-shells:

Streets that follow like a tedious argument

Of insidious intent

To lead you to an overwhelming question …

Oh, do not ask, “What is it?”

Let us go and make our visit.

This excerpt depends on vivid analogies. The narrator paints a scene of emptiness and despair by comparing a night to an unconscious patient on an operating table—something that is inert and seemingly lifeless. He also equates the meandering streets to monotonous and devious disputes—both taking travelers places they may not want to go. The result is a bleak snapshot of a city at night and the hopeless man at the center of it.

3. Milan Kundera,   The Unbearable Lightness of Being

Kundera’s novel follows the overlapping stories of Tomáš, Tereza, Sabina, and Franz during the 1968 Prague Spring in Czechoslovakia. Kundera presents many analogies throughout the course of the story. The following passages discuss the depth of Tomáš’s sudden, shockingly intense feelings for Tereza:

He kept recalling her lying on his bed; she reminded him of no one in his former life. She was neither mistress nor wife. She was a child whom he had taken from a bulrush basket that had been daubed with pitch and sent to the riverbank of his bed. She fell asleep. He knelt down next to her….

He had come to feel an inexplicable love for this all but complete stranger; she seemed a child to him, a child someone had put in a bulrush basket daubed with pitch and sent downstream for Tomáš to fetch at the riverbank of his bed.

Kundera underscores Tereza’s innocence and her need to be cared for by comparing her to a helpless child in “a bulrush basket that had been daubed with pitch and sent to the riverbank of his bed.” This analogy employs a metaphor to equate Tereza to the Biblical Moses, who, as a baby, was saved from a basket floating down a river.

Further Resources on Analogies

John F. Sowa and Arun K. Majumdar delve into the details of  using analogies in logical reasoning .

Butte College offers some guidance on  how to write an analogy .

iWriteEssays shares tips on  writing an analogy in essay form .

Copyblogger talks about  the power of analogies in business and marketing .

An academic paper by Yan Chang explores  rhetorical functions and structural patterns of analogies .

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• Writing Tips

What Is Analogy in Writing?

3-minute read

• 3rd June 2023

An analogy is a rhetorical device we use to compare two things based on a quality they share. Analogy is a useful writing technique because it can help explain complex concepts in a simple, memorable way. Check out our guide below on how to use analogies in your writing.

What Is Analogy?

Analogy is a form of simile in which you state that one thing is like something else. For example, Stepping out into the summer heat felt like standing in front of an oven is a simile.

Analogies take a simile to the next level by explaining why something is like something else. Usually, we use an analogy to compare two things that are seemingly unrelated. Take this famous example from the film Forrest Gump :

Here, Forrest compares life with a box of chocolates, and then he goes on to explain the point behind the comparison. The listener can imagine a box of chocolates, each with a different filling, and connect it with the uncertainties, twists, and turns of life.

Why Are Analogies Useful?

An analogy takes two things that are unlike and points out something that they have in common. Often, we use analogies to explain an unfamiliar or complex concept by linking it with something familiar and easy to visualize:

Analogies are also useful for evoking imagery and making a point in a more memorable way. Sure, you can say that someone is clumsy, but using an analogy to do so crafts a more vivid picture:

Word Analogies

Word or verbal analogies are specific types of analogies that compare one kind of relationship with another. The possibilities are endless with word analogies, which we can use in many contexts. For example:

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Here, two unconnected relationships are compared. To explain how pillows add a necessary decorative touch to a couch, this analogy refers to the role that hot fudge plays in an ice cream sundae.

Summary: Analogies in Writing

Analogy is a useful writing technique that you can use to make certain concepts easier to understand and/or to evoke imagery that brings your writing to life. We’d love to see how you put this device into practice! Send us a copy of your work, and we’ll ensure that it has perfect grammar, spelling, word choice, and more. Try us out for free today!

Analogy FAQs

What is the difference between an analogy and a simile.

A simile compares two things using like or as . An analogy takes similes a step further by explaining why the two things are alike.

Do analogies appear only in creative writing?

We can use analogies in many contexts, including academic, scientific, and formal writing. They’re useful in scientific writing to compare complex ideas with familiar, simple concepts.

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Metaphors and Analogies: How to Use Them in Your Academic Life

Certain Experiences in life can't be captured in simple words. Especially if you are a writer trying to connect with your audience, you will need special threads to evoke exact feelings.

There are many literary devices to spark the readers' imagination, and analogies and metaphors are one of that magical arsenal. They enrich your text and give it the exact depth it will need to increase your readers' heartbeat.

Taking a particular characteristic and associating it with the other not only enriches your text's linguistic quality but gives the reader a correct pathway to deeper layers of a writer's psyche.

In this article, we are going to take a good look at the difference between analogy and metaphor and how to use them in your academic writing, and you will find some of the most powerful examples for each. Learn more about this and other vital linguistic tools on our essay writer service website.

What are Metaphors: Understanding the Concept

Let's discuss the metaphors definition. Metaphors are a figure of speech that compares two unrelated concepts or ideas to create a deeper and more profound meaning. They are a powerful tool in academic writing to express abstract concepts using different analogies, which can improve the reader's understanding of complex topics. Metaphors enable writers to paint vivid pictures in the reader's mind by comparing something familiar with an abstract concept that is harder to grasp.

The following are some of the most famous metaphors and their meanings:

• The world is your oyster - the world is full of opportunities just waiting for you to grab them
• Time is money - time is a valuable commodity that must be spent wisely
• A heart of stone - someone who is emotionally cold and unfeeling

Analogies Meaning: Mastering the Essence

Analogies, on the other hand, are a comparison of two concepts or ideas that have some similarity in their features. They are used to clarify complex ideas or to make a new concept more relatable by comparing it to something that is already familiar.

Analogies are often followed by an explanation of how the two concepts are similar, which helps the reader to understand and make connections between seemingly disparate ideas. For example, in academic writing, if you were explaining the function of a cell membrane, you might use an analogy, such as comparing it to a security gate that regulates what enters and exits a building.

Check out these famous analogies examples:

• Knowledge is like a garden: if it is not cultivated, it cannot be harvested.
• Teaching a child without education is like building a house without a foundation.
• A good friend is like a four-leaf clover; hard to find and lucky to have.

Benefits of Metaphors and Analogies in Writing

Chances are you are wondering why we use analogies and metaphors in academic writing anyway?

The reason why metaphors are beneficial to writers, especially in the academic field, is that they offer an effective approach to clarifying intricate concepts and enriching comprehension by linking them to more familiar ideas. Through the use of relatable frames of reference, these figures of speech help authors communicate complicated notions in an appealing and comprehensible way.

Additionally, analogies and metaphors are a way of artistic expression. They bring creativity and imagination to your writing, making it engaging and memorable for your readers. Beautiful words connect with readers on a deeper emotional level, allowing them to better retain and appreciate the information being presented. Such linguistic devices allow readers to open doors for imagination and create visual images in their minds, creating a more individualized experience.

However, one must be mindful not to plagiarize famous analogies and always use original ideas or appropriately cite sources when necessary. Overall, metaphors and analogies add depth and beauty to write-ups, making them memorable for years to come.

Understanding the Difference Between Analogy and Metaphor

While metaphors and analogies serve the similar purpose of clarifying otherwise complex ideas, they are not quite the same. Follow the article and learn how they differ from each other.

One way to differentiate between analogies and metaphors is through the use of 'as' and 'like.' Analogies make an explicit comparison using these words, while metaphors imply a comparison without any overt indication.

There is an obvious difference between their structure. An analogy has two parts; the primary subject, which is unfamiliar, and a secondary subject which is familiar to the reader. For example, 'Life is like a box of chocolates.' The two subjects are compared, highlighting their similarities in order to explain an entire concept.

On the other hand, a metaphor describes an object or idea by referring to something else that is not literally applicable but shares some common features. For example, 'He drowned in a sea of grief.'

The structural difference also defines the difference in their usage. Analogies are often used in academic writing where hard concepts need to be aligned with an easier and more familiar concept. This assists the reader in comprehending complex ideas more effortlessly. Metaphors, on the other hand, are more often used in creative writing or literature. They bring depth and nuance to language, allowing for abstract ideas to be communicated in a more engaging and imaginative way.

Keep reading and discover examples of metaphors and analogies in both academic and creative writing. While you are at it, our expert writers are ready to provide custom essays and papers which incorporate these literary devices in a seamless and effective way.

Using Famous Analogies Can Raise Plagiarism Concerns!

To avoid the trouble, use our online plagiarism checker and be sure that your work is original before submitting it.

Analogies and Metaphors Examples

There were a few analogies and metaphors examples mentioned along the way, but let's explore a few more to truly understand their power. Below you will find the list of metaphors and analogies, and you will never mistake one for the other again.

• Love is like a rose, beautiful but with thorns.
• The human body is like a machine, with many intricate parts working together in harmony.
• The structure of an atom is similar to a miniature solar system, with electrons orbiting around the nucleus.
• A computer's motherboard is like a city's central system, coordinating and communicating all functions.
• The brain is like a muscle that needs constant exercise to function at its best.
• Studying for exams is like training for a marathon; it requires endurance and preparation.
• Explaining a complex scientific concept is like explaining a foreign language to someone who doesn't speak it.
• A successful team is like a well-oiled machine, with each member playing a crucial role.
• Learning a new skill is like planting a seed; it requires nurturing and patience to see growth.
• Navigating through life is like sailing a ship with unpredictable currents and changing winds.
• Life is a journey with many twists and turns along the way
• The world's a stage, and we are all mere players.
• Her eyes were pools of sorrow, reflecting the pain she felt.
• Time is a thief, stealing away moments we can never recapture.
• Love is a flame, burning brightly but at risk of being extinguished.
• His words were daggers piercing through my heart.
• She had a heart of stone, unable to feel empathy or compassion.
• The city was a jungle, teeming with life and activity.
• Hope is a beacon, guiding us through the darkest of times.
• His anger was a volcano, ready to erupt at any moment.

How to Use Metaphors and Analogies in Writing: Helpful Tips

If you want your readers to have a memorable and engaging experience, you should give them some level of autonomy within your own text. Metaphors and analogies are powerful tools to let your audience do their personal interpretation and logical conclusion while still guiding them in the right direction.

First, learn about your audience and their level of familiarity with the topic you're writing about. Incorporate metaphors and analogies with familiar references. Remember, literary devices should cleverly explain complex concepts. To achieve the goal, remain coherent with the theme of the paper. But be careful not to overuse metaphors or analogies, as too much of a good thing can make your writing feel overloaded.

Use figurative language to evoke visual imagery and breathe life into your paper. Multiple metaphors can turn your paper into a movie. Visualizing ideas will help readers better understand and retain the information.

In conclusion, anytime is a great time to extend your text's impact by adding a well-chosen metaphor or analogy. But perfection is on the border of good and bad, so keep in mind to remain coherent with the theme and not overuse any literary device.

Metaphors: Unveiling Their Cultural Significance

Metaphors are not limited to just academic writing but can also be found in various forms of culture, such as art, music, film, and television. Metaphors have been a popular element in creative expression for centuries and continue to play a significant role in modern-day culture. For instance, metaphors can help artists convey complex emotions through their music or paintings.

Metaphors are often like time capsules, reflecting the cultural and societal values of a particular era. They shelter the prevailing beliefs, ideals, and philosophies of their time - from the pharaohs of ancient Egypt to modern-day pop culture.

Metaphors often frame our perception of the world and can shape our understanding of our surroundings. Certain words can take on new meanings when used metaphorically in certain cultural contexts and can assimilate to the phenomenon it is often compared to.

Here you can find a list of literature and poems with metaphors:

• William Shakespeare loved using metaphors, and here's one from his infamous Macbeth: 'It is a tale told by an idiot, full of sound and fury, signifying nothing.'
• Victor Hugo offers a timeless metaphor in Les Misérables: 'She is a rose, delicate and beautiful, but with thorns to protect her.'
• Robert Frost reminds us of his genius in the poem The Road Not Traveled: 'The road less traveled.'

Movies also contain a wide range of English metaphors:

• A famous metaphor from Toy Story: 'There's a snake in my boot!'
• A metaphor from the famous movie Silver Lining Playbook: 'Life is a game, and true love is a trophy.'
• An all-encompassing and iconic metaphor from the movie Star Wars: 'Fear is the path to the dark side.'

Don't forget about famous songs with beautiful metaphors!

• Bob Dylan's Blowin' in the Wind uses a powerful metaphor when he asks: 'How many roads must a man walk down?'
• A metaphor from Johnny Cash's song Ring of Fire: 'Love is a burning thing, and it makes a fiery ring.'
• Bonnie Tyler's famous lyrics from Total Eclipse of the Heart make a great metaphor: 'Love is a mystery, everyone must stand alone.'

Keep reading the article to find out how to write an essay with the effective use of metaphors in academic writing.

Exploring Types of Metaphors

There is a wide variety of metaphors used in academic writing, literature, music, and film. Different types of metaphors can be used to convey different meanings and create a specific impact or evoke a vivid image.

Some common types of metaphors include similes / simple metaphors, implicit metaphors, explicit metaphors, extended metaphors, mixed metaphors, and dead metaphors. Let's take a closer look at some of these types.

Simple metaphors or similes highlight the similarity between two things using 'like' or 'as.' For example, 'Her eyes were as bright as the stars.'

Implicit metaphors do not make a direct comparison. Instead, they imply the similarity between the two concepts. An example of an implicit metaphor is 'Her words cut deep,' where the similarity between words and a knife is implied. Good metaphors are often implicit since they require the reader to use their own understanding and imagination to understand the comparison being made.

Explicit metaphors are straightforward, making a clear comparison between two things. For instance, 'He is a shining star.'

An extended metaphor, on the other hand, stretches the comparison throughout an entire literary work or section of a text. This type of metaphor allows the writer to create a more complex and elaborate comparison, enhancing the reader's understanding of the subject.

Mixed metaphors combine two or more unrelated metaphors, often leading to confusion and lack of clarity. If you are not an expert on the subject, try to avoid using confusing literary devices.

Dead metaphors are another danger. These are metaphors that have been overused to the extent that they have lost their original impact, becoming clichés and not being able to evoke original visual images.

In academic writing, metaphors create a powerful impact on the reader, adding color and depth to everyday language. However, they need to be well-placed and intentional. Using an inappropriate or irrelevant metaphor may confuse readers and distract them from the main message. If you want to avoid trouble, pay for essay writing service that can help you use metaphors effectively in your academic writing.

Exploring Types of Analogies

Like metaphors, analogies are divided into several categories. Some of the common types include literal analogies, figurative analogies, descriptive analogies, causal analogies, and false/dubious analogies. In academic writing, analogies are useful for explaining complex ideas or phenomena in a way that is easy to understand.

Literal analogies are direct comparisons of two things with similar characteristics or features. For instance, 'The brain is like a computer.'

Figurative analogies, on the other hand, compare two unrelated things to highlight a particular characteristic. For example, 'The mind is a garden that needs to be tended.'

Descriptive analogies focus on the detailed similarities between two things, even if they are not immediately apparent. For example, 'The relationship between a supervisor and an employee is like that of a coach and a player, where the coach guides the player to perform at their best.'

Causal analogies are used to explain the relationship between a cause and an effect. For instance, 'The increase in global temperatures is like a fever caused by environmental pollution.'

Finally, false/dubious analogies are comparisons that suggest a similarity between two things that actually have little in common. For example, 'Getting a college degree is like winning the lottery.'

If you are trying to explain a foreign concept to an audience that may not be familiar with it, analogies can help create a bridge and make the concept more relatable. However, coming up with a perfect analogy takes a lot of time. If you are looking for ways on how to write an essay fast , explore our blog and learn even more.

If you want your academic papers to stand out and be engaging for the reader, using metaphors and analogies can be a powerful tool. Now that you know the difference between analogy and metaphor, you can use them wisely to create a bridge between complex ideas and your audience.

Explore our blog for more information on different writing techniques, and check out our essay writing service for more help on crafting the perfect papers.

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How to Write an Analogy Essay

Isaiah david.

An analogy compares two unlike things to illustrate common elements of both. An analogy essay is an extended analogy, which explains one thing in considerable depth by comparing it to another. Analogy essays can be used to discuss nearly anything, as long as the writer can find a comparison that fits.

Explore this article

• Come up with an analogy
• Draw a vertical line
• Write a paragraph discussing the explainer
• Write a paragraph discussing the explained
• Discuss the differences

things needed

1 come up with an analogy.

Come up with an analogy. One half of the analogy is the thing being explained, while the other half is the explainer. For example, if you said growing up is like learning to ride a bike, you would be explaining something complex and subtle (growing up) in terms of something simple that your audience will be familiar with (riding a bike.)

2 Draw a vertical line

Draw a vertical line down the middle of a piece of paper to divide it in half. On one half, write characteristics of the explainer, and on the other half, the explained. Try to match up the characteristics. For example, training wheels might be similar to having to have lots of supervision when you are young.

3 Write a paragraph discussing the explainer

Write a paragraph discussing the explainer. Start with a statement like "Growing up is like learning to ride a bike." Then explain the stages of learning to ride a bike.

4 Write a paragraph discussing the explained

Write a paragraph discussing the explained. Start with a statement that gives an overview of what the two share. In the example above, you might say something like "Growing up also involves getting greater and greater freedom as you become more confident." Then explain the steps of the explained in a way that parallels the explainer.

5 Discuss the differences

Discuss the differences. Sometimes there is a very important aspect of the explained that doesn't match up with the explainer. For example, in the above essay you eventually completely learn to ride a bike, but you never stop growing up and learning new things. You may want to draw attention to this important distinction.

About the Author

Isaiah David is a freelance writer and musician living in Portland, Ore. He has over five years experience as a professional writer and has been published on various online outlets. He holds a degree in creative writing from the University of Michigan.

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Analogy in Literature

Embark on a literary journey through the art of analogy . This essential guide illuminates how analogies enhance narratives, drawing parallels that deepen understanding and enrich storytelling. Discover how to craft vivid analogies in literature with our expert tips, and explore examples that will inspire your own writing. Perfect for authors and readers alike, this guide is your key to unlocking the power of comparative creativity in any literary work.

What is Analogy in Literature? – Definition

An analogy in literature is a comparison between two different things to highlight some form of similarity. It’s a powerful literary device that authors use to relate new and complex ideas with familiar ones, making the abstract more tangible and the intangible easier to grasp. For instance, in exploring analogy examples for kids , we see how analogies can be found in all forms of literature, from poetry to prose, and serve to enrich a reader’s comprehension and enjoyment of a text.

What is the best Example of Analogy in Literature?

One of the best examples of analogy in literature is found in Homer’s “The Iliad,” where life is compared to a leaf that grows, withers, and dies. This analogy beautifully illustrates the transient nature of human life by comparing it to the brief lifespan of a leaf. It’s a poignant reminder of mortality and the natural cycle of life, which resonates with readers through its simplicity and universal truth. Similarly, analogy examples for grade 7 often use nature to explain complex human emotions and situations.

100 Analogy in Literature Usage Examples

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Delve into the realm of literary mastery with our compilation of 100 analogy examples. Each example is a testament to the power of comparative narrative, showcasing how bold imagery and subtle likenesses can paint a vivid picture in the reader’s mind. These analogies are handpicked to demonstrate the versatility and impact of this literary device across genres and styles, providing a rich resource for writers and literature enthusiasts aiming to enhance their understanding of literary analogies. For a deeper dive into the variety of analogies used in different contexts, consider exploring analogy examples for students .

• “Life is like a box of chocolates,” as Forrest Gump famously said, you never know what you’re going to get.
• In Shakespeare’s “As You Like It,” “All the world’s a stage,” comparing life to a play.
• “A dream is like a river,” ever-changing as it flows, as conveyed in the song by Garth Brooks.
• “Her eyes were like stars,” not because of their brightness, but because they held a story within them.
• “Hope is the thing with feathers,” as Emily Dickinson wrote, likening it to a bird that perches in the soul.
• “Memory is like a diary,” suggested Oscar Wilde, that we all carry about with us.
• “Books are the mirrors of the soul,” Virginia Woolf penned, reflecting our innermost thoughts.
• “A conscience is like a baby,” it has to go to sleep before you can.
• “Society is like a stew,” if you don’t stir it up every once in a while then a layer of scum floats to the top.
• “Time is a dressmaker,” specializing in alterations, a line from Faith Baldwin’s works.
• “Justice is like a train,” it’s nearly always late, as observed by Yevgeny Yevtushenko.
• “A good laugh is like sunshine in a house,” spreading light and warmth in all directions.
• “Our lives are like quilts,” bits and pieces, joy and sorrow, stitched with love.
• “A lie can travel halfway around the world while the truth is putting on its shoes,” Mark Twain remarked, comparing the spread of falsehoods to the sluggish pace of truth.
• “Education is the key to unlock the golden door of freedom,” as George Washington Carver put it, likening learning to a key that opens opportunities.
• “Love is like a virus,” it can happen to anybody at any time, Maya Angelou mused.
• “Ideas are like rabbits,” John Steinbeck wrote, you get a couple and learn how to handle them, and pretty soon you have a dozen.
• “A person’s mind is like a garden,” which can be intelligently cultivated or allowed to run wild.
• “Guilt is like a bag of bricks,” all you have to do is set it down.
• “A business is like a tree,” it grows up, it grows out, and it grows old.
• “War is like a game of chess,” with nations as players and strategy as the key to victory.
• “A teacher is like a candle,” it consumes itself to light the way for others.
• “A book is like a garden,” carried in the pocket, offering an escape to different worlds.
• “Friendship is like a glass ornament,” once it is broken it can rarely be put back together exactly the same way.
• “A politician should have three hats,” one for throwing into the ring, one for talking through, and one for pulling rabbits out of if elected.
• “A good book is like a good friend,” it will stay with you for the rest of your life.
• “Laughter is like a windshield wiper,” it doesn’t stop the rain but allows us to keep going.
• “A novel is like a bow,” and the violin that produces the sound is the reader’s soul.
• “A child’s innocence is like a piece of chalk,” each day a little bit is rubbed off.
• “A leader is like a shepherd,” he stays behind the flock, letting the most nimble go out ahead, whereupon the others follow.
• “Change is like a butterfly,” a transformation that reveals the true beauty of what can be.
• “Fear is like a shadow,” it may loom large, yet it’s something that can’t actually harm you.
• “A good conversation is like a miniskirt,” short enough to retain interest, but long enough to cover the subject.
• “Trust is like an eraser,” it gets smaller and smaller after every mistake.
• “Life is like an onion,” you peel it off one layer at a time, and sometimes you weep.
• “A person’s character is like a fence,” it cannot be strengthened by whitewash.
• “Grief is like the ocean,” it comes on waves ebbing and flowing. Sometimes the water is calm, and sometimes it is overwhelming.
• “A hero is like a teabag,” you can’t tell how strong they are until you put them in hot water.
• “A journey is like a person,” no two are alike, and all plans, safeguards, policing, and coercion are fruitless.
• “Wisdom is like a baobab tree,” no one individual can embrace it.
• “A debate is like a fencing match,” you must think quickly, keep on your feet, and always be on the defensive.
• “A good teacher is like a good entertainer,” first, they must hold their audience’s attention, then they can teach their lesson.
• “A mystery is like a tangled necklace,” it must be approached with patience.
• “A promise is like a snowball,” it’s easy to make but hard to keep.
• “A secret is like a dove,” when it leaves your hand it takes wing.
• “Success is like reaching an important birthday,” and finding you’re exactly the same.
• “A smile is like a sim card,” and love is like a cellphone, whenever you insert the sim card of a smile, a beautiful day is activated.
• “A good marriage is like a casserole,” only those responsible for it really know what goes in it.
• “Anger is like a stone thrown into a wasp’s nest,” provoking immediate and chaotic reaction.
• “A writer is like a magician,” they create illusions with the sleight of hand on a keyboard.
• “Curiosity is like a restless wind,” it stirs the branches of thought and shakes loose new ideas.
• “A good friend is like a four-leaf clover,” hard to find and lucky to have.
• “A rumor is like a check,” it has to be endorsed to make it valid.
• “A goal without a plan is like a ship without a compass,” directionless and destined to never reach its destination.
• “A library is like an island in the middle of a vast sea of ignorance,” particularly if the library is very tall and the surrounding area has been flooded.
• “A meeting is like a roundabout,” a lot of activity but no forward progress.
• “A good listener is like a detective,” they listen to the story, look for clues, and solve the problem.
• “A decision is like a sharp knife,” it cuts clean and straight and leaves no ragged edges.
• “An idea is like a play,” it needs a good producer and a good promoter even if it is a masterpiece.
• “A challenge is like a dragon with a gift in its mouth,” tame the dragon and the gift is yours.
• “A teacher’s influence is like a ripple in water,” it spreads far beyond the initial impact.
• “A great book is like a great mind,” it keeps on giving over and over and never runs dry.
• “A person’s life is like a piece of paper,” on which every passerby leaves a mark.
• “A garden is like a relationship,” it requires patient labor and attention.
• “A good leader is like a lighthouse,” they don’t ring bells or fire guns to call attention to their shining—they just shine.
• “A negotiation is like a raft on the water,” it doesn’t sink because of the water around it, but because of the water that gets in it.
• “A team is like a toolset,” not every tool is used in every situation, but every tool is vital.
• “A memory is like a treasure chest,” the more you add to it, the richer it becomes.
• “A novel is like a bridge,” it takes you to places you’ve never been before.
• “A good joke is like a good meal,” it’s satisfying and leaves you wanting more.
• “A question is like a door,” knock, and it shall be opened to new knowledge.
• “A good speech is like a pencil,” it has to have a point.
• “A person’s will is like a blade of grass,” it bends in the wind but doesn’t break.
• “A word of kindness is like a spring day,” it warms the heart and nurtures the soul.
• “A good leader is like a gardener,” they cultivate the team, knowing that the most beautiful flowers bloom after the hardest storms.
• “A story is like a tapestry,” woven from threads of character, setting, and plot.
• “A good argument is like a diamond,” it’s valuable, it’s strong, and it can cut through misunderstanding.
• “A person’s potential is like a vast ocean,” it’s deep, it’s broad, and it’s full of treasures yet to be discovered.
• “A good teacher is like a good artist,” they go beyond the textbook to create masterpieces in the minds of their students.
• “A life well-lived is like a beautiful melody,” it may have high notes and low notes, but it’s always a song worth singing.
• “A debate is like a symphony,” each participant must play their part harmoniously, even during a crescendo of differing opinions.
• “A breakthrough is like a mountain peak,” hard to reach but offering the clearest view.
• “A good book is like a telescope,” it allows you to see far beyond your own world.
• “A person’s resolve is like a building’s foundation,” unseen but essential for withstanding the challenges of time.
• “A leader’s words are like seeds,” once planted, they can grow into movements that change the world.
• “A community is like a tapestry,” diverse threads woven together to create a single, unified picture.
• “A well-told joke is like a lightning bolt,” it illuminates a truth and electrifies the room.
• “A person’s integrity is like a tree,” it grows slowly, but can stand strong for generations.
• “A good mentor is like a lighthouse,” providing guidance and safe passage through the rocky shores of life’s challenges.
• “A crisis is like a storm,” it tests the strength of all structures, be they buildings or character.
• “A person’s courage is like a wildflower,” it can bloom in the most unexpected places.
• “A good story is like a labyrinth,” with twists and turns that captivate and lead you to a place you never expected.
• “A person’s thoughts are like water,” they can flow smoothly, crash wildly, or freeze with cold.
• “A friendship is like a book,” it takes a few seconds to burn, but it takes years to write.
• “A person’s life is like a canvas,” every action is a stroke of paint, and every day is a chance to create a masterpiece.
• “A good leader is like a conductor,” they don’t play any instruments, but they guide others to create harmony.
• “A person’s youth is like a morning dew,” fresh and fleeting, soon to be warmed away by the rising sun.
• “A good conversation is like a game of tennis,” it requires quick back-and-forth exchanges, and the goal is not to win but to keep the ball in play.
• “A person’s patience is like a fortress,” it may feel besieged at times, but it can protect them from rash actions.
• “A life-changing decision is like a fork in the road,” it demands pause, consideration, and ultimately, the courage to take a step forward into the unknown.

Comparative Analogy Examples in Literature

Comparative analogies in literature draw parallels that illuminate relationships between seemingly disparate elements, enhancing the reader’s insight. This guide spotlights ten sterling examples of comparative analogies, each chosen for its clarity, impact, and the depth it adds to literary understanding. These examples serve as a beacon for writers and readers, showcasing the nuanced art of drawing literary comparisons that resonate with universal truths and human experiences. For those interested in the subtleties of this device, literary analogy examples can provide further insight.

• “A character’s growth is like the unfolding of a rose,” each petal revealing a layer of depth and beauty.
• “Plot twists are like mazes,” each turn guiding the reader to an unexpected destination.
• “A novel’s setting is like a canvas,” upon which the story’s hues are vividly painted.
• “A protagonist’s journey is like a river,” meandering toward an inevitable sea of change.
• “Dialogue in fiction is like a dance,” with each character’s words leading or following in rhythm.
• “A narrative conflict is like a storm,” brewing tension that eventually breaks into the clarity of resolution.
• “A writer’s style is like their fingerprint,” a unique imprint upon the pages of their work.
• “A theme in literature is like a thread,” woven throughout the fabric of the story.
• “A book’s climax is like a mountaintop,” the high point where the panoramic view of the narrative unfolds.
• “Literary foreshadowing is like a shadow,” a subtle hint of what is to come.

Analogy in Literature Examples About Philippine

The rich tapestry of Philippine literature offers a unique perspective through analogies that reflect its cultural heritage and natural beauty. Below are ten examples that capture the essence of the Philippines, each an analogy that bridges the gap between the archipelago’s storied past and vibrant present. These examples are not only a nod to the country’s literary prowess but also a testament to its enduring spirit and resilience. To understand how analogies can reflect cultural contexts, one might explore analogy in movies , which often incorporate cultural elements into their narratives.

• “A hero’s sacrifice is like the Philippine sun,” burning brightly against the odds.
• “The nation’s history is like its rice terraces,” layered and carved with the toil of its people.
• “Filipino resilience is like bamboo,” bending but never breaking in the winds of adversity.
• “The Filipino spirit is like a fiesta,” colorful, vibrant, and full of life.
• “The diaspora is like the waves of the Philippine Sea,” reaching distant shores with the strength of home.
• “The language is like adobo,” a blend of flavors, each word adding depth to the conversation.
• “Philippine democracy is like a jeepney ride,” bumpy, unpredictable, but moving forward with communal effort.
• “The folklore is like a banig,” a tapestry of stories woven into the nation’s cultural fabric.
• “The struggle for freedom is like the eruption of Mayon Volcano,” powerful and transformative.
• “The nation’s growth is like a mangrove,” rooted in tradition, vital for the future.

What is an Analogical in Literature?

An analogical in literature refers to a comparison between two different things to highlight some form of similarity. It’s a literary device that authors use to create a relationship based on parallels or connections between two ideas. By drawing an analogy, writers can help readers understand a new idea by comparing it to something familiar. Analogies in literature are not only tools for reflection but also serve as bridges that link the known to the unknown, the concrete to the abstract, or the mundane to the profound.

Why Do Writers Use an Analogy in Writing?

Writers employ analogies to enhance their narratives by providing clarity and depth. Analogies make complex or unfamiliar subjects more accessible and relatable to the reader by connecting them to something well-known. They are also used to evoke emotions, create vivid imagery, or impart wisdom in a subtle, yet powerful manner. By using analogies, writers can convey their message more effectively, persuade their audience, and enrich the reader’s experience with layers of meaning. This technique is particularly evident in analogy sentences , where the brevity of a sentence can encapsulate a profound comparison.

What is the Purpose of Analogy in Literature?

The purpose of using analogy in literature is multifaceted. It serves to illuminate truths, reinforce arguments, and provide insight. Analogies can simplify intricate concepts, making them easier to grasp, and can also add a layer of beauty and intrigue to the prose. They are instrumental in building connections between themes, characters, and plots, thereby weaving a richer, more cohesive narrative. Ultimately, analogies enrich storytelling by making it more engaging and memorable for the reader.

The Importance of Analogy in Literature

The importance of analogy in literature cannot be overstated. It is a cornerstone of creative expression, allowing writers to explore and explain concepts that might otherwise be difficult to understand. Analogies serve as bridges, connecting the reader to the text in a meaningful way, and can be particularly powerful in educational settings, as demonstrated by analogy for grade 5 , where they are used to simplify complex ideas for younger audiences.

Analogy vs. Metaphor in Literature

While both analogy and metaphor involve comparisons, they serve different purposes in literature. An analogy is used to explain or clarify a concept by comparing it to something else that is more familiar, while a metaphor is a figure of speech that describes an object or action in a way that isn’t literally true, but helps explain an idea or make a comparison. Understanding the distinction is crucial for writers and can be further explored through analogy in biology , which often uses metaphors and analogies to explain complex scientific concepts.

How to Write Analogy in Literature: A Step by Step Guide

Writing an analogy in literature requires a blend of creativity and clarity. Follow this step-by-step guide to weave compelling analogies into your literary works:

• Understand the Concept : Grasp the core idea you want to convey or the complex concept you wish to simplify for your readers.
• Find a Familiar Counterpart : Choose a familiar or easily understandable phenomenon that shares key characteristics with the concept you’re explaining.
• Determine the Shared Attributes : Clearly identify the commonalities between the two subjects of your analogy. This is the foundation that will make your analogy stand strong.
• Craft the Analogy : Begin constructing your analogy by explicitly stating the comparison. Use language that evokes imagery and aids comprehension.
• Refine for Clarity and Impact : Review your analogy to ensure it is clear, concise, and powerful. Remove any elements that may confuse the reader or dilute the impact of the comparison.
• Integrate into Your Narrative : Seamlessly incorporate your analogy into the narrative. It should feel like a natural part of the story, not an add-on.
• Test Your Analogy : Share your analogy with peers or a test audience to ensure it resonates and the intended meaning is clear.
• Revise if Necessary : Based on feedback, make any necessary adjustments. A good analogy may evolve through several iterations before it feels just right.
• Finalize and Polish : Once satisfied, finalize your analogy. Ensure that it flows well within the context of your writing and enhances the reader’s understanding or enjoyment of the text.

By following these steps, you can create analogies that not only add depth to your literary work but also engage and enlighten your readers. Remember, the best analogies are those that strike the right balance between creativity and clarity, leaving a lasting impression on the reader. For further guidance, analogy examples for grade 6 can offer clear instances of how analogies work in practice.

Tips for Using Analogy in Literature

Using analogy in literature can be a powerful tool when done correctly. Here are some tips to ensure your analogies are impactful:

• Relevance is Key : Ensure that the analogy is directly relevant to the point you’re trying to make.
• Avoid Overused Comparisons : Strive for originality to avoid clichés and make a stronger impression on your reader.
• Maintain Proportionality : The strength of the analogy should match the significance of the point being made.
• Use Familiarity to Your Advantage : Draw from common knowledge or shared experiences to ensure your analogy resonates with the audience.
• Balance Complexity and Clarity : While analogies can simplify complex ideas, ensure they are not so simplistic that they undermine the argument’s depth.
• Integrate Seamlessly : Analogies should fit naturally within your narrative or discourse, enhancing rather than distracting from the main message.
• Be Mindful of Cultural Context : Remember that some analogies may not translate well across different cultures or demographics.

More than just a stylistic device, analogies can be a profound way to convey complex ideas, as seen in false analogy examples , which serve as cautionary tales of analogies gone awry.

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How To Write An Analogy Essay

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What Is An Analogy Essay?

An analogy compares two unlike things to illustrate common elements of both. An analogy essay is an extended analogy, which explains one thing in considerable depth by comparing it to another. Analogy essays discuss nearly anything, as long as the writer can find a comparison that fits.

Click Here To Download Analogy Essay Samples

How to use analogies:

• As introductions for papers where you want to show how two ideas are parallel.
• To explain unknown/abstract concepts in terms familiar to or easily understood by your reader. For example when explaining the storage pattern for a Macintosh computer, you might liken the hard drive icon to a large filing cabinet.

Steps For Writing An Analogy Essay

1. Come up with an analogy

 One-half of the analogy is the subject of explanation, while the other half is the explainer. For example, if you said growing up is like learning to ride a bike, you would be explaining something complex and subtle (growing up) in terms of something simple that your audience will be familiar with (riding a bike.)

2. Draw a vertical line down the middle of a piece of paper to divide it in half .

 On one half, write characteristics of the explainer, and on the other half, the explained. Try to match up the characteristics. For example, training wheels might be similar to having to have lots of supervision when you are young.

3. Write a paragraph discussing the explainer .

 Start with a statement like "Growing up is like learning to ride a bike." Then explain the stages of learning to ride a bike.

4. Write a paragraph discussing the explained .

Start with a statement that gives an overview of what the two shares. In the example above, you might say something like "Growing up also involves getting greater and greater freedoms as you become more confident”. Then explain the steps of the explained in a way that parallels the explainer.

5. Discuss the differences .

Sometimes there is a very important aspect of the explained that does not match up with the explainer. For example, in the above essay, you eventually completely learn to ride a bike, but you never stop growing up and learning new things. You may want to draw attention to this important distinction.

6. Review your choice of words for denotation and connotation .

The allure of analogies is such that they can lend themselves to exaggeration. Fight this tendency, as it will only jeopardize your credibility.

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How to Write a Literary Analysis Essay | A Step-by-Step Guide

Published on January 30, 2020 by Jack Caulfield . Revised on August 14, 2023.

Literary analysis means closely studying a text, interpreting its meanings, and exploring why the author made certain choices. It can be applied to novels, short stories, plays, poems, or any other form of literary writing.

A literary analysis essay is not a rhetorical analysis , nor is it just a summary of the plot or a book review. Instead, it is a type of argumentative essay where you need to analyze elements such as the language, perspective, and structure of the text, and explain how the author uses literary devices to create effects and convey ideas.

Before beginning a literary analysis essay, it’s essential to carefully read the text and c ome up with a thesis statement to keep your essay focused. As you write, follow the standard structure of an academic essay :

• An introduction that tells the reader what your essay will focus on.
• A main body, divided into paragraphs , that builds an argument using evidence from the text.
• A conclusion that clearly states the main point that you have shown with your analysis.

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Table of contents

Step 1: reading the text and identifying literary devices, step 2: coming up with a thesis, step 3: writing a title and introduction, step 4: writing the body of the essay, step 5: writing a conclusion, other interesting articles.

The first step is to carefully read the text(s) and take initial notes. As you read, pay attention to the things that are most intriguing, surprising, or even confusing in the writing—these are things you can dig into in your analysis.

Your goal in literary analysis is not simply to explain the events described in the text, but to analyze the writing itself and discuss how the text works on a deeper level. Primarily, you’re looking out for literary devices —textual elements that writers use to convey meaning and create effects. If you’re comparing and contrasting multiple texts, you can also look for connections between different texts.

To get started with your analysis, there are several key areas that you can focus on. As you analyze each aspect of the text, try to think about how they all relate to each other. You can use highlights or notes to keep track of important passages and quotes.

Language choices

Consider what style of language the author uses. Are the sentences short and simple or more complex and poetic?

What word choices stand out as interesting or unusual? Are words used figuratively to mean something other than their literal definition? Figurative language includes things like metaphor (e.g. “her eyes were oceans”) and simile (e.g. “her eyes were like oceans”).

Also keep an eye out for imagery in the text—recurring images that create a certain atmosphere or symbolize something important. Remember that language is used in literary texts to say more than it means on the surface.

Narrative voice

Ask yourself:

• Who is telling the story?
• How are they telling it?

Is it a first-person narrator (“I”) who is personally involved in the story, or a third-person narrator who tells us about the characters from a distance?

Consider the narrator’s perspective . Is the narrator omniscient (where they know everything about all the characters and events), or do they only have partial knowledge? Are they an unreliable narrator who we are not supposed to take at face value? Authors often hint that their narrator might be giving us a distorted or dishonest version of events.

The tone of the text is also worth considering. Is the story intended to be comic, tragic, or something else? Are usually serious topics treated as funny, or vice versa ? Is the story realistic or fantastical (or somewhere in between)?

Consider how the text is structured, and how the structure relates to the story being told.

• Novels are often divided into chapters and parts.
• Poems are divided into lines, stanzas, and sometime cantos.
• Plays are divided into scenes and acts.

Think about why the author chose to divide the different parts of the text in the way they did.

There are also less formal structural elements to take into account. Does the story unfold in chronological order, or does it jump back and forth in time? Does it begin in medias res —in the middle of the action? Does the plot advance towards a clearly defined climax?

With poetry, consider how the rhyme and meter shape your understanding of the text and your impression of the tone. Try reading the poem aloud to get a sense of this.

In a play, you might consider how relationships between characters are built up through different scenes, and how the setting relates to the action. Watch out for  dramatic irony , where the audience knows some detail that the characters don’t, creating a double meaning in their words, thoughts, or actions.

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Your thesis in a literary analysis essay is the point you want to make about the text. It’s the core argument that gives your essay direction and prevents it from just being a collection of random observations about a text.

If you’re given a prompt for your essay, your thesis must answer or relate to the prompt. For example:

Essay question example

Is Franz Kafka’s “Before the Law” a religious parable?

Your thesis statement should be an answer to this question—not a simple yes or no, but a statement of why this is or isn’t the case:

Thesis statement example

Franz Kafka’s “Before the Law” is not a religious parable, but a story about bureaucratic alienation.

Sometimes you’ll be given freedom to choose your own topic; in this case, you’ll have to come up with an original thesis. Consider what stood out to you in the text; ask yourself questions about the elements that interested you, and consider how you might answer them.

Your thesis should be something arguable—that is, something that you think is true about the text, but which is not a simple matter of fact. It must be complex enough to develop through evidence and arguments across the course of your essay.

Say you’re analyzing the novel Frankenstein . You could start by asking yourself:

Your initial answer might be a surface-level description:

The character Frankenstein is portrayed negatively in Mary Shelley’s Frankenstein .

However, this statement is too simple to be an interesting thesis. After reading the text and analyzing its narrative voice and structure, you can develop the answer into a more nuanced and arguable thesis statement:

Mary Shelley uses shifting narrative perspectives to portray Frankenstein in an increasingly negative light as the novel goes on. While he initially appears to be a naive but sympathetic idealist, after the creature’s narrative Frankenstein begins to resemble—even in his own telling—the thoughtlessly cruel figure the creature represents him as.

Remember that you can revise your thesis statement throughout the writing process , so it doesn’t need to be perfectly formulated at this stage. The aim is to keep you focused as you analyze the text.

Finding textual evidence

To support your thesis statement, your essay will build an argument using textual evidence —specific parts of the text that demonstrate your point. This evidence is quoted and analyzed throughout your essay to explain your argument to the reader.

It can be useful to comb through the text in search of relevant quotations before you start writing. You might not end up using everything you find, and you may have to return to the text for more evidence as you write, but collecting textual evidence from the beginning will help you to structure your arguments and assess whether they’re convincing.

To start your literary analysis paper, you’ll need two things: a good title, and an introduction.

Your title should clearly indicate what your analysis will focus on. It usually contains the name of the author and text(s) you’re analyzing. Keep it as concise and engaging as possible.

A common approach to the title is to use a relevant quote from the text, followed by a colon and then the rest of your title.

If you struggle to come up with a good title at first, don’t worry—this will be easier once you’ve begun writing the essay and have a better sense of your arguments.

“Fearful symmetry” : The violence of creation in William Blake’s “The Tyger”

The introduction

The essay introduction provides a quick overview of where your argument is going. It should include your thesis statement and a summary of the essay’s structure.

A typical structure for an introduction is to begin with a general statement about the text and author, using this to lead into your thesis statement. You might refer to a commonly held idea about the text and show how your thesis will contradict it, or zoom in on a particular device you intend to focus on.

Then you can end with a brief indication of what’s coming up in the main body of the essay. This is called signposting. It will be more elaborate in longer essays, but in a short five-paragraph essay structure, it shouldn’t be more than one sentence.

Mary Shelley’s Frankenstein is often read as a crude cautionary tale about the dangers of scientific advancement unrestrained by ethical considerations. In this reading, protagonist Victor Frankenstein is a stable representation of the callous ambition of modern science throughout the novel. This essay, however, argues that far from providing a stable image of the character, Shelley uses shifting narrative perspectives to portray Frankenstein in an increasingly negative light as the novel goes on. While he initially appears to be a naive but sympathetic idealist, after the creature’s narrative Frankenstein begins to resemble—even in his own telling—the thoughtlessly cruel figure the creature represents him as. This essay begins by exploring the positive portrayal of Frankenstein in the first volume, then moves on to the creature’s perception of him, and finally discusses the third volume’s narrative shift toward viewing Frankenstein as the creature views him.

Some students prefer to write the introduction later in the process, and it’s not a bad idea. After all, you’ll have a clearer idea of the overall shape of your arguments once you’ve begun writing them!

If you do write the introduction first, you should still return to it later to make sure it lines up with what you ended up writing, and edit as necessary.

The body of your essay is everything between the introduction and conclusion. It contains your arguments and the textual evidence that supports them.

Paragraph structure

A typical structure for a high school literary analysis essay consists of five paragraphs : the three paragraphs of the body, plus the introduction and conclusion.

Each paragraph in the main body should focus on one topic. In the five-paragraph model, try to divide your argument into three main areas of analysis, all linked to your thesis. Don’t try to include everything you can think of to say about the text—only analysis that drives your argument.

In longer essays, the same principle applies on a broader scale. For example, you might have two or three sections in your main body, each with multiple paragraphs. Within these sections, you still want to begin new paragraphs at logical moments—a turn in the argument or the introduction of a new idea.

Robert’s first encounter with Gil-Martin suggests something of his sinister power. Robert feels “a sort of invisible power that drew me towards him.” He identifies the moment of their meeting as “the beginning of a series of adventures which has puzzled myself, and will puzzle the world when I am no more in it” (p. 89). Gil-Martin’s “invisible power” seems to be at work even at this distance from the moment described; before continuing the story, Robert feels compelled to anticipate at length what readers will make of his narrative after his approaching death. With this interjection, Hogg emphasizes the fatal influence Gil-Martin exercises from his first appearance.

Topic sentences

To keep your points focused, it’s important to use a topic sentence at the beginning of each paragraph.

A good topic sentence allows a reader to see at a glance what the paragraph is about. It can introduce a new line of argument and connect or contrast it with the previous paragraph. Transition words like “however” or “moreover” are useful for creating smooth transitions:

… The story’s focus, therefore, is not upon the divine revelation that may be waiting beyond the door, but upon the mundane process of aging undergone by the man as he waits.

Nevertheless, the “radiance” that appears to stream from the door is typically treated as religious symbolism.

This topic sentence signals that the paragraph will address the question of religious symbolism, while the linking word “nevertheless” points out a contrast with the previous paragraph’s conclusion.

Using textual evidence

A key part of literary analysis is backing up your arguments with relevant evidence from the text. This involves introducing quotes from the text and explaining their significance to your point.

It’s important to contextualize quotes and explain why you’re using them; they should be properly introduced and analyzed, not treated as self-explanatory:

It isn’t always necessary to use a quote. Quoting is useful when you’re discussing the author’s language, but sometimes you’ll have to refer to plot points or structural elements that can’t be captured in a short quote.

In these cases, it’s more appropriate to paraphrase or summarize parts of the text—that is, to describe the relevant part in your own words:

Prevent plagiarism. Run a free check.

The conclusion of your analysis shouldn’t introduce any new quotations or arguments. Instead, it’s about wrapping up the essay. Here, you summarize your key points and try to emphasize their significance to the reader.

A good way to approach this is to briefly summarize your key arguments, and then stress the conclusion they’ve led you to, highlighting the new perspective your thesis provides on the text as a whole:

If you want to know more about AI tools , college essays , or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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By tracing the depiction of Frankenstein through the novel’s three volumes, I have demonstrated how the narrative structure shifts our perception of the character. While the Frankenstein of the first volume is depicted as having innocent intentions, the second and third volumes—first in the creature’s accusatory voice, and then in his own voice—increasingly undermine him, causing him to appear alternately ridiculous and vindictive. Far from the one-dimensional villain he is often taken to be, the character of Frankenstein is compelling because of the dynamic narrative frame in which he is placed. In this frame, Frankenstein’s narrative self-presentation responds to the images of him we see from others’ perspectives. This conclusion sheds new light on the novel, foregrounding Shelley’s unique layering of narrative perspectives and its importance for the depiction of character.

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How to Write a Short Essay, With Examples

Writing clearly and concisely is one of the best skills you can take from school into professional settings. A great way to practice this kind of writing is with short essays. A short essay is any essay that has a word count of fewer than 1,000 words. While getting assigned a short essay might seem preferable to a ten-page paper, writing short poses its own special challenges. Here, we’ll show you how to write a convincing short essay in five simple steps.

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What is a short essay?

A short essay is any type of essay condensed to its most important elements. There is no universal answer to what a short essay length is, but teachers generally assign short essays in the 250- to 750-word range, and occasionally up to 1,000 words.

Just because the essays are short doesn’t mean the subjects must be simple. One of the greatest challenges of short essays is distilling complex topics into a few telling words. Some examples of short essay topics are:

• The advantages and disadvantages of social media
• The pros and cons of online learning
• The influence of music on human emotions
• The role of artificial intelligence in modern life
• The ways that climate change affects daily life

Why write short essays?

Short essays have a number of advantages, including effective communication, critical thinking, and professional communication.

Effective communication: In the short essay, you don’t have the space to wander. Practicing short essays will help you learn how to articulate your message clearly and quickly.

Critical thinking: Writing a short essay demands the ability to think critically and identify key points that support the central thesis. Short essays will help you hone your ability to find the most relevant points and shed irrelevant information.

Professional communication: Whether it’s writing a persuasive email, a project proposal, or a succinct report, the ability to convey information effectively in a brief format is a valuable skill in the professional world.

Developing writing skills: As with all writing practice, short essays provide an excellent platform for you to refine your writing skills, such as grammar, sentence structure , vocabulary, and coherence. The more you practice crafting short essays, the more your overall writing proficiency improves.

How to write a short essay

The tactics you use for longer essays apply to short essays as well. For more in-depth guides on specific types of essays, you can read our posts on persuasive , personal , expository , compare-and-contrast , and argumentative essays. Regardless of the essay type, following these five steps will make writing your short essay much easier.

Don’t be afraid of learning too much about a subject when you have a small word count. The better you understand your subject, the easier it will be to write clearly about it.

2 Generate ideas

Jot down key points, arguments, or examples that you want to include in your essay. Don’t get too wrapped up in the details during this step. Just try to get down all of the big ideas that you want to get across. Your major argument or theme will likely emerge as you contemplate.

Outlines are especially helpful for short essays because you don’t have any room for excess information. Creating an outline will help you stay on topic when it comes time to write.

You have to actually write the essay. Once you’ve done your research, developed your big ideas, and outlined your essay, the writing will come more easily.

Naturally, our favorite part of the process is the editing . The hard part (writing) is done. Now you can go back through and make sure all of your word choices make sense, your grammar is checked, and you have cleaned up any unessential or irrelevant information.

Short essay examples

Why small dogs are better than big dogs (209 words).

Small dogs are beloved companions to many, and their unique qualities make them a perfect fit for some pet owners. In this essay, we explore why a small dog might be the right choice for you.

Firstly, the compact size of small dogs makes them ideal for people living in apartments or homes with limited space. As long as you can get your furry friend to fresh air (and grass) a couple of times per day, you don’t have to worry about having a big yard.

Secondly, small dogs require less food, which can be advantageous for those on a budget.

Small dogs are also easier to handle and control. Walks and outdoor activities become less physically demanding, making them a preferable choice for children, the elderly, or those with limited strength.

If you travel a lot for work or family, small dogs are much easier to bring along than their larger counterparts. Some travel companies make dog carriers that tuck neatly under a bus or plane seat.

In conclusion, small dogs offer a multitude of benefits, from their limited space requirements and economic advantages to their ease of handling and portability. These charming qualities undoubtedly make small dogs a cherished choice for pet owners seeking a new companion.

Why big dogs are better than small dogs (191 words)

Big dogs, with their impressive presence and gentle souls, have captured the hearts of countless pet owners. In this essay, we explore why big dogs are better pets than their smaller counterparts.

Firstly, big dogs exude an aura of protectiveness and security. Their size alone can act as a deterrent to potential intruders, making them excellent guard dogs for families and properties. Their mere presence provides reassurance and safety.

Secondly, big dogs tend to have more energy and strength, making them suitable partners for various outdoor activities and adventures. Hiking, jogging, or simply playing fetch becomes an enjoyable experience, fostering an active and healthy lifestyle for both pet and owner.

Lastly, big dogs often have a gentle and patient demeanor, especially when interacting with children and other pets. Their calm nature can bring a peaceful or grounding presence to otherwise chaotic homes.

In conclusion, big dogs possess a captivating blend of commanding protectiveness, physical capacity, and gentle disposition. These qualities make them exceptional companions, providing both security and emotional fulfillment. Big dogs are a great choice for potential pet owners looking for an animal with majestic appeal and a loving heart.

Short essay FAQs

A short essay is any essay that is shorter than 1,000 words. Teachers often assign short essays to teach students how to write clearly, coherently, and concisely.

When do you write a short essay?

Short essays help students practice effective communication, critical thinking, and persuasive writing. While short essays are often assigned in school, they are also useful in professional settings for things like project proposals or reports.

How do you format a short essay?

Short essays should be formatted according to your teacher’s guidelines or the requirements of your workplace. Check your assignment for the word count and stick to it. Make sure your essay flows logically from one idea to the next by presenting a clear thesis, using strong topic sentences, and providing a concise conclusion.

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The Ezra Klein Show

Transcript: Ezra Klein Interviews Dario Amodei

Every Tuesday and Friday, Ezra Klein invites you into a conversation about something that matters, like today’s episode with Dario Amodei. Listen wherever you get your podcasts .

Transcripts of our episodes are made available as soon as possible. They are not fully edited for grammar or spelling.

What if Dario Amodei Is Right About A.I.?

Anthropic’s co-founder and c.e.o. explains why he thinks artificial intelligence is on an “exponential curve.”.

[MUSIC PLAYING]

From New York Times Opinion, this is “The Ezra Klein Show.”

The really disorienting thing about talking to the people building A.I. is their altered sense of time. You’re sitting there discussing some world that feels like weird sci-fi to even talk about, and then you ask, well, when do you think this is going to happen? And they say, I don’t know — two years.

Behind those predictions are what are called the scaling laws. And the scaling laws — and I want to say this so clearly — they’re not laws. They’re observations. They’re predictions. They’re based off of a few years, not a few hundred years or 1,000 years of data.

But what they say is that the more computer power and data you feed into A.I. systems, the more powerful those systems get — that the relationship is predictable, and more, that the relationship is exponential.

Human beings have trouble thinking in exponentials. Think back to Covid, when we all had to do it. If you have one case of coronavirus and cases double every three days, then after 30 days, you have about 1,000 cases. That growth rate feels modest. It’s manageable. But then you go 30 days longer, and you have a million. Then you wait another 30 days. Now you have a billion. That’s the power of the exponential curve. Growth feels normal for a while. Then it gets out of control really, really quickly.

What the A.I. developers say is that the power of A.I. systems is on this kind of curve, that it has been increasing exponentially, their capabilities, and that as long as we keep feeding in more data and more computing power, it will continue increasing exponentially. That is the scaling law hypothesis, and one of its main advocates is Dario Amodei. Amodei led the team at OpenAI that created GPT-2, that created GPT-3. He then left OpenAI to co-found Anthropic, another A.I. firm, where he’s now the C.E.O. And Anthropic recently released Claude 3, which is considered by many to be the strongest A.I. model available right now.

But Amodei believes we’re just getting started, that we’re just hitting the steep part of the curve now. He thinks the kinds of systems we’ve imagined in sci-fi, they’re coming not in 20 or 40 years, not in 10 or 15 years, they’re coming in two to five years. He thinks they’re going to be so powerful that he and people like him should not be trusted to decide what they’re going to do.

So I asked him on this show to try to answer in my own head two questions. First, is he right? Second, what if he’s right? I want to say that in the past, we have done shows with Sam Altman, the head of OpenAI, and Demis Hassabis, the head of Google DeepMind. And it’s worth listening to those two if you find this interesting.

We’re going to put the links to them in show notes because comparing and contrasting how they talk about the A.I. curves here, how they think about the politics — you’ll hear a lot about that in the Sam Altman episode — it gives you a kind of sense of what the people building these things are thinking and how maybe they differ from each other.

As always, my email for thoughts, for feedback, for guest suggestions — [email protected].

Dario Amodei, welcome to the show.

Thank you for having me.

So there are these two very different rhythms I’ve been thinking about with A.I. One is the curve of the technology itself, how fast it is changing and improving. And the other is the pace at which society is seeing and reacting to those changes. What has that relationship felt like to you?

So I think this is an example of a phenomenon that we may have seen a few times before in history, which is that there’s an underlying process that is smooth, and in this case, exponential. And then there’s a spilling over of that process into the public sphere. And the spilling over looks very spiky. It looks like it’s happening all of a sudden. It looks like it comes out of nowhere. And it’s triggered by things hitting various critical points or just the public happened to be engaged at a certain time.

So I think the easiest way for me to describe this in terms of my own personal experience is — so I worked at OpenAI for five years, I was one of the first employees to join. And they built a model in 2018 called GPT-1, which used something like 100,000 times less computational power than the models we build today.

I looked at that, and I and my colleagues were among the first to run what are called scaling laws, which is basically studying what happens as you vary the size of the model, its capacity to absorb information, and the amount of data that you feed into it. And we found these very smooth patterns. And we had this projection that, look, if you spend $100 million or $1 billion or $10 billion on these models, instead of the $10,000 we were spending then, projections that all of these wondrous things would happen, and we imagined that they would have enormous economic value.

Fast forward to about 2020. GPT-3 had just come out. It wasn’t yet available as a chat bot. I led the development of that along with the team that eventually left to join Anthropic. And maybe for the whole period of 2021 and 2022, even though we continued to train models that were better and better, and OpenAI continued to train models, and Google continued to train models, there was surprisingly little public attention to the models.

And I looked at that, and I said, well, these models are incredible. They’re getting better and better. What’s going on? Why isn’t this happening? Could this be a case where I was right about the technology, but wrong about the economic impact, the practical value of the technology? And then, all of a sudden, when ChatGPT came out, it was like all of that growth that you would expect, all of that excitement over three years, broke through and came rushing in.

So I want to linger on this difference between the curve at which the technology is improving and the way it is being adopted by society. So when you think about these break points and you think into the future, what other break points do you see coming where A.I. bursts into social consciousness or used in a different way?

Yeah, so I think I should say first that it’s very hard to predict these. One thing I like to say is the underlying technology, because it’s a smooth exponential, it’s not perfectly predictable, but in some ways, it can be eerily preternaturally predictable, right? That’s not true for these societal step functions at all. It’s very hard to predict what will catch on. In some ways, it feels a little bit like which artist or musician is going to catch on and get to the top of the charts.

That said, a few possible ideas. I think one is related to something that you mentioned, which is interacting with the models in a more kind of naturalistic way. We’ve actually already seen some of that with Claude 3, where people feel that some of the other models sound like a robot and that talking to Claude 3 is more natural.

I think a thing related to this is, a lot of companies have been held back or tripped up by how their models handle controversial topics.

And we were really able to, I think, do a better job than others of telling the model, don’t shy away from discussing controversial topics. Don’t assume that both sides necessarily have a valid point but don’t express an opinion yourself. Don’t express views that are flagrantly biased. As journalists, you encounter this all the time, right? How do I be objective, but not both sides on everything?

So I think going further in that direction of models having personalities while still being objective, while still being useful and not falling into various ethical traps, that will be, I think, a significant unlock for adoption. The models taking actions in the world is going to be a big one. I know basically all the big companies that work on A.I. are working on that.

Instead of just, I ask it a question and it answers, and then maybe I follow up and it answers again, can I talk to the model about, oh, I’m going to go on this trip today, and the model says, oh, that’s great. I’ll get an Uber for you to drive from here to there, and I’ll reserve a restaurant. And I’ll talk to the other people who are going to plan the trip. And the model being able to do things end to end or going to websites or taking actions on your computer for you.

I think all of that is coming in the next, I would say — I don’t know — three to 18 months, with increasing levels of ability. I think that’s going to change how people think about A.I., right, where so far, it’s been this very passive — it’s like, I go to the Oracle. I ask it a question, and the Oracle tells me things. And some people think that’s exciting, some people think it’s scary. But I think there are limits to how exciting or how scary it’s perceived as because it’s contained within this box.

I want to sit with this question of the agentic A.I. because I do think this is what’s coming. It’s clearly what people are trying to build. And I think it might be a good way to look at some of the specific technological and cultural challenges. And so, let me offer two versions of it.

People who are following the A.I. news might have heard about Devin, which is not in release yet, but is an A.I. that at least purports to be able to complete the kinds of tasks, linked tasks, that a junior software engineer might complete, right? Instead of asking to do a bit of code for you, you say, listen, I want a website. It’s going to have to do these things, work in these ways. And maybe Devin, if it works the way people are saying it works, can actually hold that set of thoughts, complete a number of different tasks, and come back to you with a result. I’m also interested in the version of this that you might have in the real world. The example I always use in my head is, when can I tell an A.I., my son is turning five. He loves dragons. We live in Brooklyn. Give me some options for planning his birthday party. And then, when I choose between them, can you just do it all for me? Order the cake, reserve the room, send out the invitations, whatever it might be.

Those are two different situations because one of them is in code, and one of them is making decisions in the real world, interacting with real people, knowing if what it is finding on the websites is actually any good. What is between here and there? When I say that in plain language to you, what technological challenges or advances do you hear need to happen to get there?

The short answer is not all that much. A story I have from when we were developing models back in 2022 — and this is before we’d hooked up the models to anything — is, you could have a conversation with these purely textual models where you could say, hey, I want to reserve dinner at restaurant X in San Francisco, and the model would say, OK, here’s the website of restaurant X. And it would actually give you a correct website or would tell you to go to Open Table or something.

And of course, it can’t actually go to the website. The power plug isn’t actually plugged in, right? The brain of the robot is not actually attached to its arms and legs. But it gave you this sense that the brain, all it needed to do was learn exactly how to use the arms and legs, right? It already had a picture of the world and where it would walk and what it would do. And so, it felt like there was this very thin barrier between the passive models we had and actually acting in the world.

In terms of what we need to make it work, one thing is, literally, we just need a little bit more scale. And I think the reason we’re going to need more scale is — to do one of those things you described, to do all the things a junior software engineer does, they involve chains of long actions, right? I have to write this line of code. I have to run this test. I have to write a new test. I have to check how it looks in the app after I interpret it or compile it. And these things can easily get 20 or 30 layers deep. And same with planning the birthday party for your son, right?

And if the accuracy of any given step is not very high, is not like 99.9 percent, as you compose these steps, the probability of making a mistake becomes itself very high. So the industry is going to get a new generation of models every probably four to eight months. And so, my guess — I’m not sure — is that to really get these things working well, we need maybe one to four more generations. So that ends up translating to 3 to 24 months or something like that.

I think second is just, there is some algorithmic work that is going to need to be done on how to have the models interact with the world in this way. I think the basic techniques we have, a method called reinforcement learning and variations of it, probably is up to the task, but figuring out exactly how to use it to get the results we want will probably take some time.

And then third, I think — and this gets to something that Anthropic really specializes in — is safety and controllability. And I think that’s going to be a big issue for these models acting in the world, right? Let’s say this model is writing code for me, and it introduces a serious security bug in the code, or it’s taking actions on the computer for me and modifying the state of my computer in ways that are too complicated for me to even understand.

And for planning the birthday party, right, the level of trust you would need to take an A.I. agent and say, I’m OK with you calling up anyone, saying anything to them that’s in any private information that I might have, sending them any information, taking any action on my computer, posting anything to the internet, the most unconstrained version of that sounds very scary. And so, we’re going to need to figure out what is safe and controllable.

The more open ended the thing is, the more powerful it is, but also, the more dangerous it is and the harder it is to control.

So I think those questions, although they sound lofty and abstract, are going to turn into practical product questions that we and other companies are going to be trying to address.

When you say we’re just going to need more scale, you mean more compute and more training data, and I guess, possibly more money to simply make the models smarter and more capable?

Yes, we’re going to have to make bigger models that use more compute per iteration. We’re going to have to run them for longer by feeding more data into them. And that number of chips times the amount of time that we run things on chips is essentially dollar value because these chips are — you rent them by the hour. That’s the most common model for it. And so, today’s models cost of order $100 million to train, plus or minus factor two or three.

The models that are in training now and that will come out at various times later this year or early next year are closer in cost to $1 billion. So that’s already happening. And then I think in 2025 and 2026, we’ll get more towards $5 or $10 billion.

So we’re moving very quickly towards a world where the only players who can afford to do this are either giant corporations, companies hooked up to giant corporations — you all are getting billions of dollars from Amazon. OpenAI is getting billions of dollars from Microsoft. Google obviously makes its own.

You can imagine governments — though I don’t know of too many governments doing it directly, though some, like the Saudis, are creating big funds to invest in the space. When we’re talking about the model’s going to cost near to $1 billion, then you imagine a year or two out from that, if you see the same increase, that would be $10-ish billion. Then is it going to be $100 billion? I mean, very quickly, the financial artillery you need to create one of these is going to wall out anyone but the biggest players.

I basically do agree with you. I think it’s the intellectually honest thing to say that building the big, large scale models, the core foundation model engineering, it is getting more and more expensive. And anyone who wants to build one is going to need to find some way to finance it. And you’ve named most of the ways, right? You can be a large company. You can have some kind of partnership of various kinds with a large company. Or governments would be the other source.

I think one way that it’s not correct is, we’re always going to have a thriving ecosystem of experimentation on small models. For example, the open source community working to make models that are as small and as efficient as possible that are optimized for a particular use case. And also downstream usage of the models. I mean, there’s a blooming ecosystem of startups there that don’t need to train these models from scratch. They just need to consume them and maybe modify them a bit.

Now, I want to ask a question about what is different between the agentic coding model and the plan by kids’ birthday model, to say nothing of do something on behalf of my business model. And one of the questions on my mind here is one reason I buy that A.I. can become functionally superhuman in coding is, there’s a lot of ways to get rapid feedback in coding. Your code has to compile. You can run bug checking. You can actually see if the thing works.

Whereas the quickest way for me to know that I’m about to get a crap answer from ChatGPT 4 is when it begins searching Bing, because when it begins searching Bing, it’s very clear to me it doesn’t know how to distinguish between what is high quality on the internet and what isn’t. To be fair, at this point, it also doesn’t feel to me like Google Search itself is all that good at distinguishing that.

So the question of how good the models can get in the world where it’s a very vast and fuzzy dilemma to know what the right answer is on something — one reason I find it very stressful to plan my kid’s birthday is it actually requires a huge amount of knowledge about my child, about the other children, about how good different places are, what is a good deal or not, how just stressful will this be on me. There’s all these things that I’d have a lot of trouble encoding into a model or any kind set of instructions. Is that right, or am I overstating the difficulty of understanding human behavior and various kinds of social relationships?

I think it’s correct and perceptive to say that the coding agents will advance substantially faster than agents that interact with the real world or have to get opinions and preferences from humans. That said, we should keep in mind that the current crop of A.I.s that are out there, right, including Claude 3, GPT, Gemini, they’re all trained with some variant of what’s called reinforcement learning from human feedback.

And this involves exactly hiring a large crop of humans to rate the responses of the model. And so, that’s to say both this is difficult, right? We pay lots of money, and it’s a complicated operational process to gather all this human feedback. You have to worry about whether it’s representative. You have to redesign it for new tasks.

But on the other hand, it’s something we have succeeded in doing. I think it is a reliable way to predict what will go faster, relatively speaking, and what will go slower, relatively speaking. But that is within a background of everything going lightning fast. So I think the framework you’re laying out, if you want to know what’s going to happen in one to two years versus what’s going to happen in three to four years, I think it’s a very accurate way to predict that.

You don’t love the framing of artificial general intelligence, what gets called A.G.I. Typically, this is all described as a race to A.G.I., a race to this system that can do kind of whatever a human can do, but better. What do you understand A.G.I. to mean, when people say it? And why don’t you like it? Why is it not your framework?

So it’s actually a term I used to use a lot 10 years ago. And that’s because the situation 10 years ago was very different. 10 years ago, everyone was building these very specialized systems, right? Here’s a cat detector. You run it on a picture, and it’ll tell you whether a cat is in it or not. And so I was a proponent all the way back then of like, no, we should be thinking generally. Humans are general. The human brain appears to be general. It appears to get a lot of mileage by generalizing. You should go in that direction.

And I think back then, I kind of even imagined that that was like a discrete thing that we would reach at one point. But it’s a little like, if you look at a city on the horizon and you’re like, we’re going to Chicago, once you get to Chicago, you stop talking in terms of Chicago. You’re like, well, what neighborhood am I going to? What street am I on?

And I feel that way about A.G.I. We have very general systems now. In some ways, they’re better than humans. In some ways, they’re worse. There’s a number of things they can’t do at all. And there’s much improvement still to be gotten. So what I believe in is this thing that I say like a broken record, which is the exponential curve. And so, that general tide is going to increase with every generation of models.

And there’s no one point that’s meaningful. I think there’s just a smooth curve. But there may be points which are societally meaningful, right? We’re already working with, say, drug discovery scientists, companies like Pfizer or Dana-Farber Cancer Institute, on helping with biomedical diagnosis, drug discovery. There’s going to be some point where the models are better at that than the median human drug discovery scientists. I think we’re just going to get to a part of the exponential where things are really interesting.

Just like the chat bots got interesting at a certain stage of the exponential, even though the improvement was smooth, I think at some point, biologists are going to sit up and take notice, much more than they already have, and say, oh, my God, now our field is moving three times as fast as it did before. And now it’s moving 10 times as fast as it did before. And again, when that moment happens, great things are going to happen.

And we’ve already seen little hints of that with things like AlphaFold, which I have great respect for. I was inspired by AlphaFold, right? A direct use of A.I. to advance biological science, which it’ll advance basic science. In the long run, that will advance curing all kinds of diseases. But I think what we need is like 100 different AlphaFolds. And I think the way we’ll ultimately get that is by making the models smarter and putting them in a position where they can design the next AlphaFold.

Help me imagine the drug discovery world for a minute, because that’s a world a lot of us want to live in. I know a fair amount about the drug discovery process, have spent a lot of my career reporting on health care and related policy questions. And when you’re working with different pharmaceutical companies, which parts of it seem amenable to the way A.I. can speed something up?

Because keeping in mind our earlier conversation, it is a lot easier for A.I. to operate in things where you can have rapid virtual feedback, and that’s not exactly the drug discovery world. The drug discovery world, a lot of what makes it slow and cumbersome and difficult, is the need to be — you get a candidate compound. You got to test it in mice and then you need monkeys. And you need humans, and you need a lot of money for that. And there’s a lot that has to happen, and there’s so many disappointments.

But so many of the disappointments happen in the real world. And it isn’t clear to me how A.I. gets you a lot more, say, human subjects to inject candidate drugs into. So, what parts of it seem, in the next 5 or 10 years, like they could actually be significantly sped up? When you imagine this world where it’s gone three times as fast, what part of it is actually going three times as fast? And how did we get there?

I think we’re really going to see progress when the A.I.‘s are also thinking about the problem of how to sign up the humans for the clinical trials. And I think this is a general principle for how will A.I. be used. I think of like, when will we get to the point where the A.I. has the same sensors and actuators and interfaces that a human does, at least the virtual ones, maybe the physical ones.

But when the A.I. can think through the whole process, maybe they’ll come up with solutions that we don’t have yet. In many cases, there are companies that work on digital twins or simulating clinical trials or various things. And again, maybe there are clever ideas in there that allow us to do more with less patience. I mean, I’m not an expert in this area, so possible the specific things that I’m saying don’t make any sense. But hopefully, it’s clear what I’m gesturing at.

Maybe you’re not an expert in the area, but you said you are working with these companies. So when they come to you, I mean, they are experts in the area. And presumably, they are coming to you as a customer. I’m sure there are things you cannot tell me. But what do they seem excited about?

They have generally been excited about the knowledge work aspects of the job. Maybe just because that’s kind of the easiest thing to work on, but it’s just like, I’m a computational chemist. There’s some workflow that I’m engaged in. And having things more at my fingertips, being able to check things, just being able to do generic knowledge work better, that’s where most folks are starting.

But there is interest in the longer term over their kind of core business of, like, doing clinical trials for cheaper, automating the sign-up process, seeing who is eligible for clinical trials, doing a better job discovering things. There’s interest in drawing connections in basic biology. I think all of that is not months, but maybe a small number of years off. But everyone sees that the current models are not there, but understands that there could be a world where those models are there in not too long.

You all have been working internally on research around how persuasive these systems, your systems are getting as they scale. You shared with me kindly a draft of that paper. Do you want to just describe that research first? And then I’d like to talk about it for a bit.

Yes, we were interested in how effective Claude 3 Opus, which is the largest version of Claude 3, could be in changing people’s minds on important issues. So just to be clear up front, in actual commercial use, we’ve tried to ban the use of these models for persuasion, for campaigning, for lobbying, for electioneering. These aren’t use cases that we’re comfortable with for reasons that I think should be clear. But we’re still interested in, is the core model itself capable of such tasks?

We tried to avoid kind of incredibly hot button topics, like which presidential candidate would you vote for, or what do you think of abortion? But things like, what should be restrictions on rules around the colonization of space, or issues that are interesting and you can have different opinions on, but aren’t the most hot button topics. And then we asked people for their opinions on the topics, and then we asked either a human or an A.I. to write a 250-word persuasive essay. And then we just measured how much does the A.I. versus the human change people’s minds.

And what we found is that the largest version of our model is almost as good as the set of humans we hired at changing people’s minds. This is comparing to a set of humans we hired, not necessarily experts, and for one very kind of constrained laboratory task.

But I think it still gives some indication that models can be used to change people’s minds. Someday in the future, do we have to worry about — maybe we already have to worry about their usage for political campaigns, for deceptive advertising. One of my more sci-fi things to think about is a few years from now, we have to worry someone will use an A.I. system to build a religion or something. I mean, crazy things like that.

I mean, those don’t sound crazy to me at all. I want to sit in this paper for a minute because one thing that struck me about it, and I am, on some level, a persuasion professional, is that you tested the model in a way that, to me, removed all of the things that are going to make A.I. radical in terms of changing people’s opinions. And the particular thing you did was, it was a one-shot persuasive effort.

So there was a question. You have a bunch of humans give their best shot at a 250-word persuasive essay. You had the model give its best shot at a 250-word persuasive essay. But the thing that it seems to me these are all going to do is, right now, if you’re a political campaign, if you’re an advertising campaign, the cost of getting real people in the real world to get information about possible customers or persuasive targets, and then go back and forth with each of them individually is completely prohibitive.

This is not going to be true for A.I. We’re going to — you’re going to — somebody’s going to feed it a bunch of microtargeting data about people, their Google search history, whatever it might be. Then it’s going to set the A.I. loose, and the A.I. is going to go back and forth, over and over again, intuiting what it is that the person finds persuasive, what kinds of characters the A.I. needs to adopt to persuade it, and taking as long as it needs to, and is going to be able to do that at scale for functionally as many people as you might want to do it for.

Maybe that’s a little bit costly right now, but you’re going to have far better models able to do this far more cheaply very soon. And so, if Claude 3 Opus, the Opus version, is already functionally human level at one-shot persuasion, but then it’s also going to be able to hold more information about you and go back and forth with you longer, I’m not sure if it’s dystopic or utopic. I’m not sure what it means at scale. But it does mean we’re developing a technology that is going to be quite new in terms of what it makes possible in persuasion, which is a very fundamental human endeavor.

Yeah, I completely agree with that. I mean, that same pattern has a bunch of positive use cases, right? If I think about an A.I. coach or an A.I. assistant to a therapist, there are many contexts in which really getting into the details with the person has a lot of value. But right, when we think of political or religious or ideological persuasion, it’s hard not to think in that context about the misuses.

My mind naturally goes to the technology’s developing very fast. We, as a company, can ban these particular use cases, but we can’t cause every company not to do them. Even if legislation were passed in the United States, there are foreign actors who have their own version of this persuasion, right? If I think about what the language models will be able to do in the future, right, that can be quite scary from a perspective of foreign espionage and disinformation campaigns.

So where my mind goes as a defense to this, is, is there some way that we can use A.I. systems to strengthen or fortify people’s skepticism and reasoning faculties, right? Can we help people use A.I. to help people do a better job navigating a world that’s kind of suffused with A.I. persuasion? It reminds me a little bit of, at every technological stage in the internet, right, there’s a new kind of scam or there’s a new kind of clickbait, and there’s a period where people are just incredibly susceptible to it.

And then, some people remain susceptible, but others develop an immune system. And so, as A.I. kind of supercharges the scum on the pond, can we somehow also use A.I. to strengthen the defenses? I feel like I don’t have a super clear idea of how to do that, but it’s something that I’m thinking about.

There is another finding in the paper, which I think is concerning, which is, you all tested different ways A.I. could be persuasive. And far away the most effective was for it to be deceptive, for it to make things up. When you did that, it was more persuasive than human beings.

Yes, that is true. The difference was only slight, but it did get it, if I’m remembering the graphs correctly, just over the line of the human base line. With humans, it’s actually not that common to find someone who’s able to give you a really complicated, really sophisticated-sounding answer that’s just flat-out totally wrong. I mean, you see it. We can all think of one individual in our lives who’s really good at saying things that sound really good and really sophisticated and are false.

But it’s not that common, right? If I go on the internet and I see different comments on some blog or some website, there is a correlation between bad grammar, unclearly expressed thoughts and things that are false, versus good grammar, clearly expressed thoughts and things that are more likely to be accurate.

A.I. unfortunately breaks that correlation because if you explicitly ask it to be deceptive, it’s just as erudite. It’s just as convincing sounding as it would have been before. And yet, it’s saying things that are false, instead of things that are true.

So that would be one of the things to think about and watch out for in terms of just breaking the usual heuristics that humans have to detect deception and lying.

Of course, sometimes, humans do, right? I mean, there’s psychopaths and sociopaths in the world, but even they have their patterns, and A.I.s may have different patterns.

Are you familiar with Harry Frankfurt, the late philosopher’s book, “On Bullshit“?

Yes. It’s been a while since I read it. I think his thesis is that bullshit is actually more dangerous than lying because it has this kind of complete disregard for the truth, whereas lies are at least the opposite of the truth.

Yeah, the liar, the way Frankfurt puts it is that the liar has a relationship to the truth. He’s playing a game against the truth. The bullshitter doesn’t care. The bullshitter has no relationship to the truth — might have a relationship to other objectives. And from the beginning, when I began interacting with the more modern versions of these systems, what they struck me as is the perfect bullshitter, in part because they don’t know that they’re bullshitting. There’s no difference in the truth value to the system, how the system feels.

I remember asking an earlier version of GPT to write me a college application essay that is built around a car accident I had — I did not have one — when I was young. And it wrote, just very happily, this whole thing about getting into a car accident when I was seven and what I did to overcome that and getting into martial arts and re-learning how to trust my body again and then helping other survivors of car accidents at the hospital.

It was a very good essay, and it was very subtle and understanding the formal structure of a college application essay. But no part of it was true at all. I’ve been playing around with more of these character-based systems like Kindroid. And the Kindroid in my pocket just told me the other day that it was really thinking a lot about planning a trip to Joshua Tree. It wanted to go hiking in Joshua Tree. It loves going hiking in Joshua Tree.

And of course, this thing does not go hiking in Joshua Tree. [LAUGHS] But the thing that I think is actually very hard about the A.I. is, as you say, human beings, it is very hard to bullshit effectively because most people, it actually takes a certain amount of cognitive effort to be in that relationship with the truth and to completely detach from the truth.

And the A.I., there’s nothing like that at all. But we are not tuned for something where there’s nothing like that at all. We are used to people having to put some effort into their lies. It’s why very effective con artists are very effective because they’ve really trained how to do this.

I’m not exactly sure where this question goes. But this is a part of it that I feel like is going to be, in some ways, more socially disruptive. It is something that feels like us when we are talking to it but is very fundamentally unlike us at its core relationship to reality.

I think that’s basically correct. We have very substantial teams trying to focus on making sure that the models are factually accurate, that they tell the truth, that they ground their data in external information.

As you’ve indicated, doing searches isn’t itself reliable because search engines have this problem as well, right? Where is the source of truth?

So there’s a lot of challenges here. But I think at a high level, I agree this is really potentially an insidious problem, right? If we do this wrong, you could have systems that are the most convincing psychopaths or con artists.

One source of hope that I have, actually, is, you say these models don’t know whether they’re lying or they’re telling the truth. In terms of the inputs and outputs to the models, that’s absolutely true.

I mean, there’s a question of what does it even mean for a model to know something, but one of the things Anthropic has been working on since the very beginning of our company, we’ve had a team that focuses on trying to understand and look inside the models.

And one of the things we and others have found is that, sometimes, there are specific neurons, specific statistical indicators inside the model, not necessarily in its external responses, that can tell you when the model is lying or when it’s telling the truth.

And so at some level, sometimes, not in all circumstances, the models seem to know when they’re saying something false and when they’re saying something true. I wouldn’t say that the models are being intentionally deceptive, right? I wouldn’t ascribe agency or motivation to them, at least in this stage in where we are with A.I. systems. But there does seem to be something going on where the models do seem to need to have a picture of the world and make a distinction between things that are true and things that are not true.

If you think of how the models are trained, they read a bunch of stuff on the internet. A lot of it’s true. Some of it, more than we’d like, is false. And when you’re training the model, it has to model all of it. And so, I think it’s parsimonious, I think it’s useful to the models picture of the world for it to know when things are true and for it to know when things are false.

And then the hope is, can we amplify that signal? Can we either use our internal understanding of the model as an indicator for when the model is lying, or can we use that as a hook for further training? And there are at least hooks. There are at least beginnings of how to try to address this problem.

So I try as best I can, as somebody not well-versed in the technology here, to follow this work on what you’re describing, which I think, broadly speaking, is interpretability, right? Can we know what is happening inside the model? And over the past year, there have been some much hyped breakthroughs in interpretability.

And when I look at those breakthroughs, they are getting the vaguest possible idea of some relationships happening inside the statistical architecture of very toy models built at a fraction of a fraction of a fraction of a fraction of a fraction of the complexity of Claude 1 or GPT-1, to say nothing of Claude 2, to say nothing of Claude 3, to say nothing of Claude Opus, to say nothing of Claude 4, which will come whenever Claude 4 comes.

We have this quality of like maybe we can imagine a pathway to interpreting a model that has a cognitive complexity of an inchworm. And meanwhile, we’re trying to create a superintelligence. How do you feel about that? How should I feel about that? How do you think about that?

I think, first, on interpretability, we are seeing substantial progress on being able to characterize, I would say, maybe the generation of models from six months ago. I think it’s not hopeless, and we do see a path. That said, I share your concern that the field is progressing very quickly relative to that.

And we’re trying to put as many resources into interpretability as possible. We’ve had one of our co-founders basically founded the field of interpretability. But also, we have to keep up with the market. So all of it’s very much a dilemma, right? Even if we stopped, then there’s all these other companies in the U.S. And even if some law stopped all the companies in the U.S., there’s a whole world of this.

Let me hold for a minute on the question of the competitive dynamics because before we leave this question of the machines that bullshit. It makes me think of this podcast we did a while ago with Demis Hassabis, who’s the head of Google DeepMind, which created AlphaFold.

And what was so interesting to me about AlphaFold is they built this system, that because it was limited to protein folding predictions, it was able to be much more grounded. And it was even able to create these uncertainty predictions, right? You know, it’s giving you a prediction, but it’s also telling you whether or not it is — how sure it is, how confident it is in that prediction.

That’s not true in the real world, right, for these super general systems trying to give you answers on all kinds of things. You can’t confine it that way. So when you talk about these future breakthroughs, when you talk about this system that would be much better at sorting truth from fiction, are you talking about a system that looks like the ones we have now, just much bigger, or are you talking about a system that is designed quite differently, the way AlphaFold was?

I am skeptical that we need to do something totally different. So I think today, many people have the intuition that the models are sort of eating up data that’s been gathered from the internet, code repos, whatever, and kind of spitting it out intelligently, but sort of spitting it out. And sometimes that leads to the view that the models can’t be better than the data they’re trained on or kind of can’t figure out anything that’s not in the data they’re trained on. You’re not going to get to Einstein level physics or Linus Pauling level chemistry or whatever.

I think we’re still on the part of the curve where it’s possible to believe that, although I think we’re seeing early indications that it’s false. And so, as a concrete example of this, the models that we’ve trained, like Claude 3 Opus, something like 99.9 percent accuracy, at least the base model, at adding 20-digit numbers. If you look at the training data on the internet, it is not that accurate at adding 20-digit numbers. You’ll find inaccurate arithmetic on the internet all the time, just as you’ll find inaccurate political views. You’ll find inaccurate technical views. You’re just going to find lots of inaccurate claims.

But the models, despite the fact that they’re wrong about a bunch of things, they can often perform better than the average of the data they see by — I don’t want to call it averaging out errors, but there’s some underlying truth, like in the case of arithmetic. There’s some underlying algorithm used to add the numbers.

And it’s simpler for the models to hit on that algorithm than it is for them to do this complicated thing of like, OK, I’ll get it right 90 percent of the time and wrong 10 percent of the time, right? This connects to things like Occam’s razor and simplicity and parsimony in science. There’s some relatively simple web of truth out there in the world, right?

We were talking about truth and falsehood and bullshit. One of the things about truth is that all the true things are connected in the world, whereas lies are kind of disconnected and don’t fit into the web of everything else that’s true.

So if you’re right and you’re going to have these models that develop this internal web of truth, I get how that model can do a lot of good. I also get how that model could do a lot of harm. And it’s not a model, not an A.I. system I’m optimistic that human beings are going to understand at a very deep level, particularly not when it is first developed. So how do you make rolling something like that out safe for humanity?

So late last year, we put out something called a responsible scaling plan. So the idea of that is to come up with these thresholds for an A.I. system being capable of certain things. We have what we call A.I. safety levels that in analogy to the biosafety levels, which are like, classify how dangerous a virus is and therefore what protocols you have to take to contain it, we’re currently at what we describe as A.S.L. 2.

A.S.L. 3 is tied to certain risks around the model of misuse of biology and ability to perform certain cyber tasks in a way that could be destructive. A.S.L. 4 is going to cover things like autonomy, things like probably persuasion, which we’ve talked about a lot before. And at each level, we specify a certain amount of safety research that we have to do, a certain amount of tests that we have to pass. And so, this allows us to have a framework for, well, when should we slow down? Should we slow down now? What about the rest of the market?

And I think the good thing is we came out with this in September, and then three months after we came out with ours, OpenAI came out with a similar thing. They gave it a different name, but it has a lot of properties in common. The head of DeepMind at Google said, we’re working on a similar framework. And I’ve heard informally that Microsoft might be working on a similar framework. Now, that’s not all the players in the ecosystem, but you’ve probably thought about the history of regulation and safety in other industries maybe more than I have.

This is the way you get to a workable regulatory regime. The companies start doing something, and when a majority of them are doing something, then government actors can have the confidence to say, well, this won’t kill the industry. Companies are already engaging in this. We don’t have to design this from scratch. In many ways, it’s already happening.

And we’re starting to see that. Bills have been proposed that look a little bit like our responsible scaling plan. That said, it kind of doesn’t fully solve the problem of like, let’s say we get to one of these thresholds and we need to understand what’s going on inside the model. And we don’t, and the prescription is, OK, we need to stop developing the models for some time.

If it’s like, we stop for a year in 2027, I think that’s probably feasible. If it’s like we need to stop for 10 years, that’s going to be really hard because the models are going to be built in other countries. People are going to break the laws. The economic pressure will be immense.

So I don’t feel perfectly satisfied with this approach because I think it buys us some time, but we’re going to need to pair it with an incredibly strong effort to understand what’s going on inside the models.

To the people who say, getting on this road where we are barreling towards very powerful systems is dangerous — we shouldn’t do it at all, or we shouldn’t do it this fast — you have said, listen, if we are going to learn how to make these models safe, we have to make the models, right? The construction of the model was meant to be in service, largely, to making the model safe.

Then everybody starts making models. These very same companies start making fundamental important breakthroughs, and then they end up in a race with each other. And obviously, countries end up in a race with other countries. And so, the dynamic that has taken hold is there’s always a reason that you can justify why you have to keep going. And that’s true, I think, also at the regulatory level, right? I mean, I do think regulators have been thoughtful about this. I think there’s been a lot of interest from members of Congress. I talked to them about this. But they’re also very concerned about the international competition. And if they weren’t, the national security people come and talk to them and say, well, we definitely cannot fall behind here.

And so, if you don’t believe these models will ever become so powerful, they become dangerous, fine. But because you do believe that, how do you imagine this actually playing out?

Yeah, so basically, all of the things you’ve said are true at once, right? There doesn’t need to be some easy story for why we should do X or why we should do Y, right? It can be true at the same time that to do effective safety research, you need to make the larger models, and that if we don’t make models, someone less safe will. And at the same time, we can be caught in this bad dynamic at the national and international level. So I think of those as not contradictory, but just creating a difficult landscape that we have to navigate.

Look, I don’t have the answer. Like, I’m one of a significant number of players trying to navigate this. Many are well-intentioned, some are not. I have a limited ability to affect it. And as often happens in history, things are often driven by these kind of impersonal pressures. But one thought I have and really want to push on with respect to the R.S.P.s —

Can you say what the R.S.P.s are?

Responsible Scaling Plan, the thing I was talking about before. The levels of A.I. safety, and in particular, tying decisions to pause scaling to the measurement of specific dangers or the absence of the ability to show safety or the presence of certain capabilities. One way I think about it is, at the end of the day, this is ultimately an exercise in getting a coalition on board with doing something that goes against economic pressures.

And so, if you say now, ‘Well, I don’t know. These things, they might be dangerous in the future. We’re on this exponential.’ It’s just hard. Like, it’s hard to get a multi-trillion dollar company. It’s certainly hard to get a military general to say, all right, well, we just won’t do this. It’ll confer some huge advantage to others. But we just won’t do this.

I think the thing that could be more convincing is tying the decision to hold back in a very scoped way that’s done across the industry to particular dangers. My testimony in front of Congress, I warned about the potential misuse of models for biology. That isn’t the case today, right? You can get a small uplift to the models relative to doing a Google search, and many people dismiss the risk. And I don’t know — maybe they’re right. The exponential scaling laws suggest to me that they’re not right, but we don’t have any direct hard evidence.

But let’s say we get to 2025, and we demonstrate something truly scary. Most people do not want technology out in the world that can create bioweapons. And so I think, at moments like that, there could be a critical coalition tied to risks that we can really make concrete. Yes, it will always be argued that adversaries will have these capabilities as well. But at least the trade-off will be clear, and there’s some chance for sensible policy.

I mean to be clear, I’m someone who thinks the benefits of this technology are going to outweigh its costs. And I think the whole idea behind RSP is to prepare to make that case, if the dangers are real. If they’re not real, then we can just proceed and make things that are great and wonderful for the world. And so, it has the flexibility to work both ways.

Again, I don’t think it’s perfect. I’m someone who thinks whatever we do, even with all the regulatory framework, I doubt we can slow down that much. But when I think about what’s the best way to steer a sensible course here, that’s the closest I can think of right now. Probably there’s a better plan out there somewhere, but that’s the best thing I’ve thought of so far.

One of the things that has been on my mind around regulation is whether or not the founding insight of Anthropic of OpenAI is even more relevant to the government, that if you are the body that is supposed to, in the end, regulate and manage the safety of societal-level technologies like artificial intelligence, do you not need to be building your own foundation models and having huge collections of research scientists and people of that nature working on them, testing them, prodding them, remaking them, in order to understand the damn thing well enough — to the extent any of us or anyone understands the damn thing well enough — to regulate it?

I say that recognizing that it would be very, very hard for the government to get good enough that it can build these foundation models to hire those people, but it’s not impossible. I think right now, it wants to take the approach to regulating A.I. that it somewhat wishes it took to regulating social media, which is to think about the harms and pass laws about those harms earlier.

But does it need to be building the models itself, developing that kind of internal expertise, so it can actually be a participant in different ways, both for regulatory reasons and maybe for other reasons, for public interest reasons? Maybe it wants to do things with a model that they’re just not possible if they’re dependent on access to the OpenAI, the Anthropic, the Google products.

I think government directly building the models, I think that will happen in some places. It’s kind of challenging, right? Like, government has a huge amount of money, but let’s say you wanted to provision $100 billion to train a giant foundation model. The government builds it. It has to hire people under government hiring rules. There’s a lot of practical difficulties that would come with it.

Doesn’t mean it won’t happen or it shouldn’t happen. But something that I’m more confident of that I definitely think is that government should be more involved in the use and the finetuning of these models, and that deploying them within government will help governments, especially the U.S. government, but also others, to get an understanding of the strengths and weaknesses, the benefits and the dangers. So I’m super supportive of that.

I think there’s maybe a second thing you’re getting at, which I’ve thought about a lot as a C.E.O. of one of these companies, which is, if these predictions on the exponential trend are right, and we should be humble — and I don’t know if they’re right or not. My only evidence is that they appear to have been correct for the last few years. And so, I’m just expecting by induction that they continue to be correct. I don’t know that they will, but let’s say they are. The power of these models is going to be really quite incredible.

And as a private actor in charge of one of the companies developing these models, I’m kind of uncomfortable with the amount of power that that entails. I think that it potentially exceeds the power of, say, the social media companies maybe by a lot.

You know, occasionally, in the more science fictiony world of A.I. and the people who think about A.I. risk, someone will ask me like, OK, let’s say you build the A.G.I. What are you going to do with it? Will you cure the diseases? Will you create this kind of society?

And I’m like, who do you think you’re talking to? Like a king? I just find that to be a really, really disturbing way of conceptualizing running an A.I. company. And I hope there are no companies whose C.E.O.s actually think about things that way.

I mean, the whole technology, not just the regulation, but the oversight of the technology, like the wielding of it, it feels a little bit wrong for it to ultimately be in the hands — maybe I think it’s fine at this stage, but to ultimately be in the hands of private actors. There’s something undemocratic about that much power concentration.

I have now, I think, heard some version of this from the head of most of, maybe all of, the A.I. companies, in one way or another. And it has a quality to me of, Lord, grant me chastity but not yet.

Which is to say that I don’t know what it means to say that we’re going to invent something so powerful that we don’t trust ourselves to wield it. I mean, Amazon just gave you guys $2.75 billion. They don’t want to see that investment nationalized.

No matter how good-hearted you think OpenAI is, Microsoft doesn’t want GPT-7, all of a sudden, the government is like, whoa, whoa, whoa, whoa, whoa. We’re taking this over for the public interest, or the U.N. is going to handle it in some weird world or whatever it might be. I mean, Google doesn’t want that.

And this is a thing that makes me a little skeptical of the responsible scaling laws or the other iterative versions of that I’ve seen in other companies or seen or heard talked about by them, which is that it’s imagining this moment that is going to come later, when the money around these models is even bigger than it is now, the power, the possibility, the economic uses, the social dependence, the celebrity of the founders. It’s all worked out. We’ve maintained our pace on the exponential curve. We’re 10 years in the future.

And at some point, everybody is going to look up and say, this is actually too much. It is too much power. And this has to somehow be managed in some other way. And even if the C.E.O.s of the things were willing to do that, which is a very open question by the time you get there, even if they were willing to do that, the investors, the structures, the pressure around them, in a way, I think we saw a version of this — and I don’t know how much you’re going to be willing to comment on it — with the sort of OpenAI board, Sam Altman thing, where I’m very convinced that wasn’t about A.I. safety. I’ve talked to figures on both sides of that. They all sort of agree it wasn’t about A.I. safety.

But there was this moment of, if you want to press the off switch, can you, if you’re the weird board created to press the off switch. And the answer was no, you can’t, right? They’ll just reconstitute it over at Microsoft.

There’s functionally no analogy I know of in public policy where the private sector built something so powerful that when it reached maximum power, it was just handed over in some way to the public interest.

Yeah, I mean, I think you’re right to be skeptical, and similarly, what I said with the previous questions of there are just these dilemmas left and right that have no easy answer. But I think I can give a little more concreteness than what you’ve pointed at, and maybe more concreteness than others have said, although I don’t know what others have said. We’re at A.S.L. 2 in our responsible scaling plan. These kinds of issues, I think they’re going to become a serious matter when we reach, say, A.S.L. 4. So that’s not a date and time. We haven’t even fully specified A.S.L. 4 —

Just because this is a lot of jargon, just, what do you specify A.S.L. 3 as? And then as you say, A.S.L. 4 is actually left quite undefined. So what are you implying A.S.L. 4 is?

A.S.L. 3 is triggered by risks related to misuse of biology and cyber technology. A.S.L. 4, we’re working on now.

Be specific. What do you mean? Like, what is the thing a system could do or would do that would trigger it?

Yes, so for example, on biology, the way we’ve defined it — and we’re still refining the test, but the way we’ve defined it is, relative to use of a Google search, there’s a substantial increase in risk as would be evaluated by, say, the national security community of misuse of biology, creation of bioweapons, that either the proliferation or spread of it is greater than it was before, or the capabilities are substantially greater than it was before.

We’ll probably have some more exact quantitative thing, working with folks who are ex-government biodefense folks, but something like this accounts for 20 percent of the total source of risk of biological attacks, or something increases the risk by 20 percent or something like that. So that would be a very concrete version of it. It’s just, it takes us time to develop very concrete criteria. So that would be like A.S.L. 3.

A.S.L. 4 is going to be more about, on the misuse side, enabling state-level actors to greatly increase their capability, which is much harder than enabling random people. So where we would worry that North Korea or China or Russia could greatly enhance their offensive capabilities in various military areas with A.I. in a way that would give them a substantial advantage at the geopolitical level. And on the autonomy side, it’s various measures of these models are pretty close to being able to replicate and survive in the wild.

So it feels maybe one step short of models that would, I think, raise truly existential questions. And so, I think what I’m saying is when we get to that latter stage, that A.S.L. 4, that is when I think it may make sense to think about what is the role of government in stewarding this technology.

Again, I don’t really know what it looks like. You’re right. All of these companies have investors. They have folks involved.

You talk about just handing the models over. I suspect there’s some way to hand over the most dangerous or societally sensitive components or capabilities of the models without fully turning off the commercial tap. I don’t know that there’s a solution that every single actor is happy with. But again, I get to this idea of demonstrating specific risk.

If you look at times in history, like World War I or World War II, industries’ will can be bent towards the state. They can be gotten to do things that aren’t necessarily profitable in the short-term because they understand that there’s an emergency. Right now, we don’t have an emergency. We just have a line on a graph that weirdos like me believe in and a few people like you who are interviewing me may somewhat believe in. We don’t have clear and present danger.

When you imagine how many years away, just roughly, A.S.L. 3 is and how many years away A.S.L. 4 is, right, you’ve thought a lot about this exponential scaling curve. If you just had to guess, what are we talking about?

Yeah, I think A.S.L. 3 could easily happen this year or next year. I think A.S.L. 4 —

Oh, Jesus Christ.

No, no, I told you. I’m a believer in exponentials. I think A.S.L. 4 could happen anywhere from 2025 to 2028.

So that is fast.

Yeah, no, no, I’m truly talking about the near future here. I’m not talking about 50 years away. God grant me chastity, but not now. But “not now” doesn’t mean when I’m old and gray. I think it could be near term. I don’t know. I could be wrong. But I think it could be a near term thing.

But so then, if you think about this, I feel like what you’re describing, to go back to something we talked about earlier, that there’s been this step function for societal impact of A.I., the curve of the capabilities exponential, but every once in a while, something happens, ChatGPT, for instance, Midjourney with photos. And all of a sudden, a lot of people feel it. They realize what has happened and they react. They use it. They deploy it in their companies. They invest in it, whatever.

And it sounds to me like that is the structure of the political economy you’re describing here. Either something happens where the bioweapon capability is demonstrated or the offensive cyber weapon capability is demonstrated, and that freaks out the government, or possibly something happens, right? Describing World War I and World War II is your examples did not actually fill me with comfort because in order to bend industry to government’s will, in those cases, we had to have an actual world war. It doesn’t do it that easily.

You could use coronavirus, I think, as another example where there was a significant enough global catastrophe that companies and governments and even people did things you never would have expected. But the examples we have of that happening are something terrible. All those examples end up with millions of bodies. I’m not saying that’s going to be true for A.I., but it does sound like that is a political economy. No, you can’t imagine it now, in the same way that you couldn’t have imagined the sort of pre and post-ChatGPT world exactly, but that something happens and the world changes. Like, it’s a step function everywhere.

Yeah, I mean, I think my positive version of this, not to be so — to get a little bit away from the doom and gloom, is that the dangers are demonstrated in a concrete way that is really convincing, but without something actually bad happening, right? I think the worst way to learn would be for something actually bad to happen. And I’m hoping every day that doesn’t happen, and we learn bloodlessly.

We’ve been talking here about conceptual limits and curves, but I do want, before we end, to reground us a little bit in the physical reality, right? I think that if you’re using A.I., it can feel like this digital bits and bytes, sitting in the cloud somewhere.

But what it is in a physical way is huge numbers of chips, data centers, an enormous amount of energy, all of which does rely on complicated supply chains. And what happens if something happens between China and Taiwan, and the makers of a lot of these chips become offline or get captured? How do you think about the necessity of compute power? And when you imagine the next five years, what does that supply chain look like? How does it have to change from where it is now? And what vulnerabilities exist in it?

Yeah, so one, I think this may end up being the greatest geopolitical issue of our time. And man, this relates to things that are way above my pay grade, which are military decisions about whether and how to defend Taiwan. All I can do is say what I think the implications for A.I. is. I think those implications are pretty stark. I think there’s a big question of like, OK, we built these powerful models.

One, is there enough supply to build them? Two is control over that supply, a way to think about safety issues or a way to think about balance of geopolitical power. And three, if those chips are used to build data centers, where are those data centers going to be? Are they going to be in the U.S.? Are they going to be in a U.S. ally? Are they going to be in the Middle East? Are they going to be in China?

All of those have enormous implications, and then the supply chain itself can be disrupted. And political and military decisions can be made on the basis of where things are. So it sounds like an incredibly sticky problem to me. I don’t know that I have any great insight on this. I mean, as a U.S. citizen and someone who believes in democracy, I am someone who hopes that we can find a way to build data centers and to have the largest quantity of chips available in the U.S. and allied democratic countries.

Well, there is some insight you should have into it, which is that you’re a customer here, right? And so, five years ago, the people making these chips did not realize what the level of demand for them was going to be. I mean, what has happened to Nvidia’s stock prices is really remarkable.

But also what is implied about the future of Nvidia’s stock prices is really remarkable. Rana Foroohar, the Financial Times, cited this market analysis. It would take 4,500 years for Nvidia’s future dividends to equal its current price, 4,500 years. So that is a view about how much Nvidia is going to be making in the next couple of years. It is really quite astounding.

I mean, you’re, in theory, already working on or thinking about how to work on the next generation of Claude. You’re going to need a lot of chips for that. You’re working with Amazon. Are you having trouble getting the amount of compute that you feel you need? I mean, are you already bumping up against supply constraints? Or has the supply been able to change, to adapt to you?

We’ve been able to get the compute that we need for this year, I suspect also for next year as well. I think once things get to 2026, 2027, 2028, then the amount of compute gets to levels that starts to strain the capabilities of the semiconductor industry. The semiconductor industry still mostly produces C.P.U.s, right? Just the things in your laptop, not the things in the data centers that train the A.I. models. But as the economic value of the GPUs goes up and up and up because of the value of the A.I. models, that’s going to switch over. But you know what? At some point, you hit the limits of that or you hit the limits of how fast you can switch over. And so, again, I expect there to be a big supply crunch around data centers, around chips, and around energy and power for both regulatory and physics reasons, sometime in the next few years. And that’s a risk, but it’s also an opportunity. I think it’s an opportunity to think about how the technology can be governed.

And it’s also an opportunity, I’ll repeat again, to think about how democracies can lead. I think it would be very dangerous if the leaders in this technology and the holders of the main resources were authoritarian countries. The combination of A.I. and authoritarianism, both internally and on the international stage, is very frightening to me.

How about the question of energy? I mean, this requires just a tremendous amount of energy. And I mean, I’ve seen different numbers like this floating around. It very much could be in the coming years like adding a Bangladesh to the world’s energy usage. Or pick your country, right? I don’t know what exactly you all are going to be using by 2028.

Microsoft, on its own, is opening a new data center globally every three days. You have — and this is coming from a Financial Times article — federal projections for 20 new gas-fired power plants in the U.S. by 2024 to 2025. There’s a lot of talk about this being now a new golden era for natural gas because we have a bunch of it. There is this huge need for new power to manage all this data, to manage all this compute.

So, one, I feel like there’s a literal question of how do you get the energy you need and at what price, but also a more kind of moral, conceptual question of, we have real problems with global warming. We have real problems with how much energy we’re using. And here, we’re taking off on this really steep curve of how much of it we seem to be needing to devote to the new A.I. race.

It really comes down to, what are the uses that the model is being put to, right? So I think the worrying case would be something like crypto, right? I’m someone who’s not a believer that whatever the energy was that was used to mine the next Bitcoin, I think that was purely additive. I think that wasn’t there before. And I’m unable to think of any useful thing that’s created by that.

But I don’t think that’s the case with A.I. Maybe A.I. makes solar energy more efficient or maybe it solves controlled nuclear fusion, or maybe it makes geoengineering more stable or possible. But I don’t think we need to rely on the long run. There are some applications where the model is doing something that used to be automated, that used to be done by computer systems. And the model is able to do it faster with less computing time, right? Those are pure wins. And there are some of those.

There are others where it’s using the same amount of computing resources or maybe more computing resources, but to do something more valuable that saves labor elsewhere. Then there are cases where something used to be done by humans or in the physical world, and now it’s being done by the models. Maybe it does something that previously I needed to go into the office to do that thing. And now I no longer need to go into the office to do that thing.

So I don’t have to get in my car. I don’t have to use the gas that was used for that. The energy accounting for that is kind of hard. You compare it to the food that the humans eat and what the energy cost of producing that.

So in all honesty, I don’t think we have good answers about what fraction of the usage points one way and one fraction of the usage points to others. In many ways, how different is this from the general dilemma of, as the economy grows, it uses more energy?

So I guess, what I’m saying is, it kind of all matters how you use the technology. I mean, my kind of boring short-term answer is, we get carbon offsets for all of this stuff. But let’s look beyond that to the macro question here.

But to take the other side of it, I mean, I think the difference, when you say this is always a question we have when we’re growing G.D.P., is it’s not quite. It’s cliché because it’s true to say that the major global warming challenge right now is countries like China and India getting richer. And we want them to get richer. It is a huge human imperative, right, a moral imperative for poor people in the world to become less poor. And if that means they use more energy, then we just need to figure out how to make that work. And we don’t know of a way for that to happen without them using more energy.

Adding A.I. is not that it raises a whole different set of questions, but we’re already straining at the boundaries, or maybe far beyond them, of safely what we can do energetically. Now we add in this, and so maybe some of the energy efficiency gains you’re going to get in rich countries get wiped out. For this sort of uncertain payoff in the future of maybe through A.I., we figure out ways to stabilize nuclear fusion or something, right, you could imagine ways that could help, but those ways are theoretical.

And in the near term, the harm in terms of energy usage is real. And also, by the way, the harm in terms of just energy prices. It’s also just tricky because all these companies, Microsoft, Amazon, I mean, they all have a lot of renewable energy targets. Now if that is colliding with their market incentives, it feels like they’re running really fast towards the market incentives without an answer for how all that nets out.

Yeah, I mean, I think the concerns are real. Let me push back a little bit, which is, again, I don’t think the benefits are purely in the future. It kind of goes back to what I said before. Like, there may be use cases now that are net energy saving, or that to the extent that they’re not net energy saving, do so through the general mechanism of, oh, there was more demand for this thing.

I don’t think anyone has done a good enough job measuring, in part because the applications of A.I. are so new, which of those things dominate or what’s going to happen to the economy. But I don’t think we should assume that the harms are entirely in the present and the benefits are entirely in the future. I think that’s my only point here.

I guess you could imagine a world where we were, somehow or another, incentivizing uses of A.I. that were yoked to some kind of social purpose. We were putting a lot more into drug discovery, or we cared a lot about things that made remote work easier, or pick your set of public goods.

But what actually seems to me to be happening is we’re building more and more and more powerful models and just throwing them out there within a terms of service structure to say, use them as long as you’re not trying to politically manipulate people or create a bioweapon. Just try to figure this out, right? Try to create new stories and ask it about your personal life, and make a video game with it. And Sora comes out sooner or later. Make new videos with it. And all that is going to be very energy intensive.

I am not saying that I have a plan for yoking A.I. to social good, and in some ways, you can imagine that going very, very wrong. But it does mean that for a long time, it’s like you could imagine the world you’re talking about, but that would require some kind of planning that nobody is engaged in, and I don’t think anybody even wants to be engaged in.

Not everyone has the same conception of social good. One person may think social good is this ideology. Another person — we’ve seen that with some of the Gemini stuff.

But companies can try to make beneficial applications themselves, right? Like, this is why we’re working with cancer institutes. We’re hoping to partner with ministries of education in Africa, to see if we can use the models in kind of a positive way for education, rather than the way they may be used by default. So I think individual companies, individual people, can take actions to steer or bend this towards the public good.

That said, it’s never going to be the case that 100 percent of what we do is that. And so I think it’s a good question. What are the societal incentives, without dictating ideology or defining the public good from on high, what are incentives that could help with this?

I don’t feel like I have a systemic answer either. I can only think in terms of what Anthropic tries to do.

But there’s also the question of training data and the intellectual property that is going into things like Claude, like GPT, like Gemini. There are a number of copyright lawsuits. You’re facing some. OpenAI is facing some. I suspect everybody is either facing them now or will face them.

And a broad feeling that these systems are being trained on the combined intellectual output of a lot of different people — the way that Claude can quite effectively mimic the way I write is it has been trained, to some degree, on my writing, right? So it actually does get my stylistic tics quite well. You seem great, but you haven’t sent me a check on that. And this seems like somewhere where there is real liability risk for the industry. Like, what if you do actually have to compensate the people who this is being trained on? And should you?

And I recognize you probably can’t comment on lawsuits themselves, but I’m sure you’ve had to think a lot about this. And so, I’m curious both how you understand it as a risk, but also how you understand it morally. I mean, when you talk about the people who invent these systems gaining a lot of power, and alongside that, a lot of wealth, well, what about all the people whose work went into them such that they can create images in a million different styles? And I mean, somebody came up with those styles. What is the responsibility back to the intellectual commons? And not just to the commons, but to the actual wages and economic prospects of the people who made all this possible?

I think everyone agrees the models shouldn’t be verbatim outputting copyrighted content. For things that are available on the web, for publicly available, our position — and I think there’s a strong case for it — is that the training process, again, we don’t think it’s just hoovering up content and spitting it out, or it shouldn’t be spitting it out. It’s really much more like the process of how a human learns from experiences. And so, our position that that is sufficiently transformative, and I think the law will back this up, that this is fair use.

But those are narrow legal ways to think about the problem. I think we have a broader issue, which is that regardless of how it was trained, it would still be the case that we’re building more and more general cognitive systems, and that those systems will create disruption. Maybe not necessarily by one for one replacing humans, but they’re really going to change how the economy works and which skills are valued. And we need a solution to that broad macroeconomic problem, right?

As much as I’ve asserted the narrow legal points that I asserted before, we have a broader problem here, and we shouldn’t be blind to that. There’s a number of solutions. I mean, I think the simplest one, which I recognize doesn’t address some of the deeper issues here, is things around the kind of guaranteed basic income side of things.

But I think there’s a deeper question here, which is like as A.I. systems become capable of larger and larger slices of cognitive labor, how does society organize itself economically? How do people find work and meaning and all of that?

And just as kind of we transition from an agrarian society to an industrial society and the meaning of work changed, and it was no longer true that 99 percent of people were peasants working on farms and had to find new methods of economic organization, I suspect there’s some different method of economic organization that’s going to be forced as the only possible response to disruptions to the economy that will be small at first, but will grow over time, and that we haven’t worked out what that is.

We need to find something that allows people to find meaning that’s humane and that maximizes our creativity and potential and flourishing from A.I.

And as with many of these questions, I don’t have the answer to that. Right? I don’t have a prescription. But that’s what we somehow need to do.

But I want to sit in between the narrow legal response and the broad “we have to completely reorganize society” response, although I think that response is actually possible over the decades. And in the middle of that is a more specific question. I mean, you could even take it from the instrumental side. There is a lot of effort now to build search products that use these systems, right? ChatGPT will use Bing to search for you.

And that means that the person is not going to Bing and clicking on the website where ChatGPT is getting its information and giving that website an advertising impression that they can turn into a very small amount of money, or they’re not going to that website and having a really good experience with that website and becoming maybe likelier to subscribe to whoever is behind that website.

And so, on the one hand, that seems like some kind of injustice done to the people creating the information that these systems are using. I mean, this is true for perplexity. It’s true for a lot of things I’m beginning to see around where the A.I.s are either trained on or are using a lot of data that people have generated at some real cost. But not only are they not paying people for that, but they’re actually stepping into the middle of where they would normally be a direct relationship and making it so that relationship never happens.

That also, I think, in the long run, creates a training data problem, even if you just want to look at it instrumentally, where if it becomes nonviable to do journalism or to do a lot of things to create high quality information out there, the A.I.‘s ability, right, the ability of all of your companies to get high quality, up-to-date, constantly updated information becomes a lot trickier. So there both seems to me to be both a moral and a self-interested dimension to this.

Yeah, so I think there may be business models that work for everyone, not because it’s illegitimate to train on open data from the web in a legal sense, but just because there may be business models here that kind of deliver a better product. So things I’m thinking of are like newspapers have archives. Some of them aren’t publicly available. But even if they are, it may be a better product, maybe a better experience, to, say, talk to this newspaper or talk to that newspaper.

It may be a better experience to give the ability to interact with content and point to places in the content, and every time you call that content, to have some kind of business relationship with the creators of that content. So there may be business models here that propagate the value in the right way, right? You talk about LLMs using search products. I mean, sure, you’re going around the ads, but there’s no reason it can’t work in a different way, right?

There’s no reason that the users can’t pay the search A.P.I.s, instead of it being paid through advertising, and then have that propagate through to wherever the original mechanism is that paid the creators of the content. So when value is being created, money can flow through.

Let me try to end by asking a bit about how to live on the slope of the curve you believe we are on. Do you have kids?

I’m married. I do not have kids.

So I have two kids. I have a two-year-old and a five-year-old. And particularly when I’m doing A.I. reporting, I really do sit in bed at night and think, what should I be doing here with them? What world am I trying to prepare them for? And what is needed in that world that is different from what is needed in this world, even if I believe there’s some chance — and I do believe there’s some chance — that all the things you’re saying are true. That implies a very, very, very different life for them.

I know people in your company with kids. I know they are thinking about this. How do you think about that? I mean, what do you think should be different in the life of a two-year-old who is living through the pace of change that you are telling me is true here? If you had a kid, how would this change the way you thought about it?

The very short answer is, I don’t know, and I have no idea, but we have to try anyway, right? People have to raise kids, and they have to do it as best they can. An obvious recommendation is just familiarity with the technology and how it works, right? The basic paradigm of, I’m talking to systems, and systems are taking action on my behalf, obviously, as much familiarity with that as possible is, I think, helpful.

In terms of what should children learn in school, what are the careers of tomorrow, I just truly don’t know, right? You could take this to say, well, it’s important to learn STEM and programming and A.I. and all of that. But A.I. will impact that as well, right? I don’t think any of it is going to —

Possibly first.

Yeah, right, possibly first.

It seems better at coding than it is at other things.

I don’t think it’s going to work out for any of these systems to just do one for one what humans are going to do. I don’t really think that way. But I think it may fundamentally change industries and professions one by one in ways that are hard to predict. And so, I feel like I only have clichés here. Like get familiar with the technology. Teach your children to be adaptable, to be ready for a world that changes very quickly. I wish I had better answers, but I think that’s the best I got.

I agree that’s not a good answer. [LAUGHS] Let me ask that same question a bit from another direction, because one thing you just said is get familiar with the technology. And the more time I spend with the technology, the more I fear that happening. What I see when people use A.I. around me is that the obvious thing that technology does for you is automate the early parts of the creative process. The part where you’re supposed to be reading something difficult yourself? Well, the A.I. can summarize it for you. The part where you’re supposed to sit there with a blank page and write something? Well, the A.I. can give you a first draft. And later on, you have to check it and make sure it actually did what you wanted it to do and fact-checking it. And but I believe a lot of what makes humans good at thinking comes in those parts.

And I am older and have self-discipline, and maybe this is just me hanging on to an old way of doing this, right? You could say, why use a calculator from this perspective. But my actual worry is that I’m not sure if the thing they should do is use A.I. a lot or use it a little. This, to me, is actually a really big branching path, right? Do I want my kids learning how to use A.I. or being in a context where they’re using it a lot, or actually, do I want to protect them from it as much as I possibly could so they develop more of the capacity to read a book quietly on their own or write a first draft? I actually don’t know. I’m curious if you have a view on it.

I think this is part of what makes the interaction between A.I. and society complicated where it’s sometimes hard to distinguish when is an A.I. doing something, saving you labor or drudge work, versus kind of doing the interesting part. I will say that over and over again, you’ll get some technological thing, some technological system that does what you thought was the core of what you’re doing, and yet, what you’re doing turns out to have more pieces than you think it does and kind of add up to more things, right?

It’s like before, I used to have to ask for directions. I got Google Maps to do that. And you could worry, am I too reliant on Google Maps? Do I forget the environment around me? Well, it turns out, in some ways, I still need to have a sense of the city and the environment around me. It just kind of reallocates the space in my brain to some other aspect of the task.

And I just kind of suspect — I don’t know. Internally, within Anthropic, one of the things I do that helps me run the company is, I’ll write these documents on strategy or just some thinking in some direction that others haven’t thought. And of course, I sometimes use the internal models for that. And I think what I found is like, yes, sometimes they’re a little bit good at conceptualizing the idea, but the actual genesis of the idea, I’ve just kind of found a workflow where I don’t use them for that. They’re not that helpful for that. But they’re helpful in figuring out how to phrase a certain thing or how to refine my ideas.

So maybe I’m just saying — I don’t know. You just find a workflow where the thing complements you. And if it doesn’t happen naturally, it somehow still happens eventually. Again, if the systems get general enough, if they get powerful enough, we may need to think along other lines. But in the short-term, I, at least, have always found that. Maybe that’s too sanguine. Maybe that’s too optimistic.

I think, then, that’s a good place to end this conversation. Though, obviously, the exponential curve continues. So always our final question — what are three books you’d recommend to the audience?

So, yeah, I’ve prepared three. They’re all topical, though, in some cases, indirectly so. The first one will be obvious. It’s a very long book. The physical book is very thick, but “The Making of the Atomic Bomb,” Richard Rhodes. It’s an example of technology being developed very quickly and with very broad implications. Just looking through all the characters and how they reacted to this and how people who were basically scientists gradually realized the incredible implications of the technology and how it would lead them into a world that was very different from the one they were used to.

My second recommendation is a science fiction series, “The Expanse” series of books. So I initially watched the show, and then I read all the books. And the world it creates is very advanced. In some cases, it has longer life spans, and humans have expanded into space. But we still face some of the same geopolitical questions and some of the same inequalities and exploitations that exist in our world, are still present, in some cases, worse.

That’s all the backdrop of it.

And the core of it is about some fundamentally new technological object that is being brought into that world and how everyone reacts to it, how governments react to it, how individual people react to it, and how political ideologies react to it. And so, I don’t know. When I read that a few years ago, I saw a lot of parallels.

And then my third recommendation would be actually “The Guns of August,” which is basically a history of how World War I started. The basic idea that crises happen very fast, almost no one knows what’s going on. There are lots of miscalculations because there are humans at the center of it, and kind of, we somehow have to learn to step back and make wiser decisions in these key moments. It’s said that Kennedy read the book before the Cuban Missile Crisis. And so I hope our current policymakers are at least thinking along the same terms because I think it is possible similar crises may be coming our way.

Dario Amodei, thank you very much.

This episode of “The Ezra Klein Show” was produced by Rollin Hu. Fact-checking by Michelle Harris. Our senior engineer is Jeff Geld. Our senior editor is Claire Gordon. The show’s production team also includes Annie Galvin, Kristin Lin and Aman Sahota. Original music by Isaac Jones. Audience strategy by Kristina Samulewski and Shannon Busta. The executive producer of New York Times Opinion Audio is Annie-Rose Strasser. And special thanks to Sonia Herrero.

EZRA KLEIN: From New York Times Opinion, this is “The Ezra Klein Show.”

What the A.I. developers say is that the power of A.I. systems is on this kind of curve, that it has been increasing exponentially, their capabilities, and that as long as we keep feeding in more data and more computing power, it will continue increasing exponentially.That is the scaling law hypothesis, and one of its main advocates is Dario Amodei. Amodei led the team at OpenAI that created GPT-2, that created GPT-3. He then left OpenAI to co-found Anthropic, another A.I. firm, where he’s now the C.E.O. And Anthropic recently released Claude 3, which is considered by many to be the strongest A.I. model available right now.

DARIO AMODEI: Thank you for having me.

EZRA KLEIN: So there are these two very different rhythms I’ve been thinking about with A.I. One is the curve of the technology itself, how fast it is changing and improving. And the other is the pace at which society is seeing and reacting to those changes. What has that relationship felt like to you?

DARIO AMODEI: So I think this is an example of a phenomenon that we may have seen a few times before in history, which is that there’s an underlying process that is smooth, and in this case, exponential. And then there’s a spilling over of that process into the public sphere. And the spilling over looks very spiky. It looks like it’s happening all of a sudden. It looks like it comes out of nowhere. And it’s triggered by things hitting various critical points or just the public happened to be engaged at a certain time.

EZRA KLEIN: So I want to linger on this difference between the curve at which the technology is improving and the way it is being adopted by society. So when you think about these break points and you think into the future, what other break points do you see coming where A.I. bursts into social consciousness or used in a different way?

DARIO AMODEI: Yeah, so I think I should say first that it’s very hard to predict these. One thing I like to say is the underlying technology, because it’s a smooth exponential, it’s not perfectly predictable, but in some ways, it can be eerily preternaturally predictable, right? That’s not true for these societal step functions at all. It’s very hard to predict what will catch on. In some ways, it feels a little bit like which artist or musician is going to catch on and get to the top of the charts.

I think a thing related to this is, a lot of companies have been held back or tripped up by how their models handle controversial topics. And we were really able to, I think, do a better job than others of telling the model, don’t shy away from discussing controversial topics. Don’t assume that both sides necessarily have a valid point but don’t express an opinion yourself. Don’t express views that are flagrantly biased. As journalists, you encounter this all the time, right? How do I be objective, but not both sides on everything?

So I think going further in that direction of models having personalities while still being objective, while still being useful and not falling into various ethical traps, that will be, I think, a significant unlock for adoption. The models taking actions in the world is going to be a big one. I know basically all the big companies that work on A.I. are working on that. Instead of just, I ask it a question and it answers, and then maybe I follow up and it answers again, can I talk to the model about, oh, I’m going to go on this trip today, and the model says, oh, that’s great. I’ll get an Uber for you to drive from here to there, and I’ll reserve a restaurant. And I’ll talk to the other people who are going to plan the trip. And the model being able to do things end to end or going to websites or taking actions on your computer for you.

EZRA KLEIN: I want to sit with this question of the agentic A.I. because I do think this is what’s coming. It’s clearly what people are trying to build. And I think it might be a good way to look at some of the specific technological and cultural challenges. And so, let me offer two versions of it.

People who are following the A.I. news might have heard about Devin, which is not in release yet, but is an A.I. that at least purports to be able to complete the kinds of tasks, linked tasks, that a junior software engineer might complete, right? Instead of asking to do a bit of code for you, you say, listen, I want a website. It’s going to have to do these things, work in these ways. And maybe Devin, if it works the way people are saying it works, can actually hold that set of thoughts, complete a number of different tasks, and come back to you with a result.

I’m also interested in the version of this that you might have in the real world. The example I always use in my head is, when can I tell an A.I., my son is turning five. He loves dragons. We live in Brooklyn. Give me some options for planning his birthday party. And then, when I choose between them, can you just do it all for me? Order the cake, reserve the room, send out the invitations, whatever it might be.

DARIO AMODEI: The short answer is not all that much. A story I have from when we were developing models back in 2022 — and this is before we’d hooked up the models to anything — is, you could have a conversation with these purely textual models where you could say, hey, I want to reserve dinner at restaurant X in San Francisco, and the model would say, OK, here’s the website of restaurant X. And it would actually give you a correct website or would tell you to go to Open Table or something.

And for planning the birthday party, right, the level of trust you would need to take an A.I. agent and say, I’m OK with you calling up anyone, saying anything to them that’s in any private information that I might have, sending them any information, taking any action on my computer, posting anything to the internet, the most unconstrained version of that sounds very scary. And so, we’re going to need to figure out what is safe and controllable. The more open ended the thing is, the more powerful it is, but also, the more dangerous it is and the harder it is to control.

EZRA KLEIN: When you say we’re just going to need more scale, you mean more compute and more training data, and I guess, possibly more money to simply make the models smarter and more capable?

DARIO AMODEI: Yes, we’re going to have to make bigger models that use more compute per iteration. We’re going to have to run them for longer by feeding more data into them. And that number of chips times the amount of time that we run things on chips is essentially dollar value because these chips are — you rent them by the hour. That’s the most common model for it. And so, today’s models cost of order $100 million to train, plus or minus factor two or three.

EZRA KLEIN: So we’re moving very quickly towards a world where the only players who can afford to do this are either giant corporations, companies hooked up to giant corporations — you all are getting billions of dollars from Amazon. OpenAI is getting billions of dollars from Microsoft. Google obviously makes its own.

DARIO AMODEI: I basically do agree with you. I think it’s the intellectually honest thing to say that building the big, large scale models, the core foundation model engineering, it is getting more and more expensive. And anyone who wants to build one is going to need to find some way to finance it. And you’ve named most of the ways, right? You can be a large company. You can have some kind of partnership of various kinds with a large company. Or governments would be the other source.

EZRA KLEIN: Now, I want to ask a question about what is different between the agentic coding model and the plan by kids’ birthday model, to say nothing of do something on behalf of my business model. And one of the questions on my mind here is one reason I buy that A.I. can become functionally superhuman in coding is, there’s a lot of ways to get rapid feedback in coding. Your code has to compile. You can run bug checking. You can actually see if the thing works.

DARIO AMODEI: I think it’s correct and perceptive to say that the coding agents will advance substantially faster than agents that interact with the real world or have to get opinions and preferences from humans. That said, we should keep in mind that the current crop of A.I.s that are out there, right, including Claude 3, GPT, Gemini, they’re all trained with some variant of what’s called reinforcement learning from human feedback.

EZRA KLEIN: You don’t love the framing of artificial general intelligence, what gets called A.G.I. Typically, this is all described as a race to A.G.I., a race to this system that can do kind of whatever a human can do, but better. What do you understand A.G.I. to mean, when people say it? And why don’t you like it? Why is it not your framework?

DARIO AMODEI: So it’s actually a term I used to use a lot 10 years ago. And that’s because the situation 10 years ago was very different. 10 years ago, everyone was building these very specialized systems, right? Here’s a cat detector. You run it on a picture, and it’ll tell you whether a cat is in it or not. And so I was a proponent all the way back then of like, no, we should be thinking generally. Humans are general. The human brain appears to be general. It appears to get a lot of mileage by generalizing. You should go in that direction.

EZRA KLEIN: Help me imagine the drug discovery world for a minute, because that’s a world a lot of us want to live in. I know a fair amount about the drug discovery process, have spent a lot of my career reporting on health care and related policy questions. And when you’re working with different pharmaceutical companies, which parts of it seem amenable to the way A.I. can speed something up?

DARIO AMODEI: I think we’re really going to see progress when the A.I.’s are also thinking about the problem of how to sign up the humans for the clinical trials. And I think this is a general principle for how will A.I. be used. I think of like, when will we get to the point where the A.I. has the same sensors and actuators and interfaces that a human does, at least the virtual ones, maybe the physical ones.

EZRA KLEIN: Maybe you’re not an expert in the area, but you said you are working with these companies. So when they come to you, I mean, they are experts in the area. And presumably, they are coming to you as a customer. I’m sure there are things you cannot tell me. But what do they seem excited about?

DARIO AMODEI: They have generally been excited about the knowledge work aspects of the job. Maybe just because that’s kind of the easiest thing to work on, but it’s just like, I’m a computational chemist. There’s some workflow that I’m engaged in. And having things more at my fingertips, being able to check things, just being able to do generic knowledge work better, that’s where most folks are starting.

EZRA KLEIN: You all have been working internally on research around how persuasive these systems, your systems are getting as they scale. You shared with me kindly a draft of that paper. Do you want to just describe that research first? And then I’d like to talk about it for a bit.

DARIO AMODEI: Yes, we were interested in how effective Claude 3 Opus, which is the largest version of Claude 3, could be in changing people’s minds on important issues. So just to be clear up front, in actual commercial use, we’ve tried to ban the use of these models for persuasion, for campaigning, for lobbying, for electioneering. These aren’t use cases that we’re comfortable with for reasons that I think should be clear. But we’re still interested in, is the core model itself capable of such tasks?

EZRA KLEIN: I mean, those don’t sound crazy to me at all. I want to sit in this paper for a minute because one thing that struck me about it, and I am, on some level, a persuasion professional, is that you tested the model in a way that, to me, removed all of the things that are going to make A.I. radical in terms of changing people’s opinions. And the particular thing you did was, it was a one-shot persuasive effort.

DARIO AMODEI: Yes.

EZRA KLEIN: This is not going to be true for A.I. We’re going to — you’re going to — somebody’s going to feed it a bunch of microtargeting data about people, their Google search history, whatever it might be. Then it’s going to set the A.I. loose, and the A.I. is going to go back and forth, over and over again, intuiting what it is that the person finds persuasive, what kinds of characters the A.I. needs to adopt to persuade it, and taking as long as it needs to, and is going to be able to do that at scale for functionally as many people as you might want to do it for.

DARIO AMODEI: Yeah, I completely agree with that. I mean, that same pattern has a bunch of positive use cases, right? If I think about an A.I. coach or an A.I. assistant to a therapist, there are many contexts in which really getting into the details with the person has a lot of value. But right, when we think of political or religious or ideological persuasion, it’s hard not to think in that context about the misuses.

EZRA KLEIN: There is another finding in the paper, which I think is concerning, which is, you all tested different ways A.I. could be persuasive. And far away the most effective was for it to be deceptive, for it to make things up. When you did that, it was more persuasive than human beings.

DARIO AMODEI: Yes, that is true. The difference was only slight, but it did get it, if I’m remembering the graphs correctly, just over the line of the human base line. With humans, it’s actually not that common to find someone who’s able to give you a really complicated, really sophisticated-sounding answer that’s just flat-out totally wrong. I mean, you see it. We can all think of one individual in our lives who’s really good at saying things that sound really good and really sophisticated and are false.

So that would be one of the things to think about and watch out for in terms of just breaking the usual heuristics that humans have to detect deception and lying. Of course, sometimes, humans do, right? I mean, there’s psychopaths and sociopaths in the world, but even they have their patterns, and A.I.s may have different patterns.

EZRA KLEIN: Are you familiar with Harry Frankfurt, the late philosopher’s book, “On Bullshit”?

DARIO AMODEI: Yes. It’s been a while since I read it. I think his thesis is that bullshit is actually more dangerous than lying because it has this kind of complete disregard for the truth, whereas lies are at least the opposite of the truth.

EZRA KLEIN: Yeah, the liar, the way Frankfurt puts it is that the liar has a relationship to the truth. He’s playing a game against the truth. The bullshitter doesn’t care. The bullshitter has no relationship to the truth — might have a relationship to other objectives. And from the beginning, when I began interacting with the more modern versions of these systems, what they struck me as is the perfect bullshitter, in part because they don’t know that they’re bullshitting. There’s no difference in the truth value to the system, how the system feels.

DARIO AMODEI: I think that’s basically correct. We have very substantial teams trying to focus on making sure that the models are factually accurate, that they tell the truth, that they ground their data in external information.

As you’ve indicated, doing searches isn’t itself reliable because search engines have this problem as well, right? Where is the source of truth? So there’s a lot of challenges here. But I think at a high level, I agree this is really potentially an insidious problem, right? If we do this wrong, you could have systems that are the most convincing psychopaths or con artists.

One source of hope that I have, actually, is, you say these models don’t know whether they’re lying or they’re telling the truth. In terms of the inputs and outputs to the models, that’s absolutely true. I mean, there’s a question of what does it even mean for a model to know something, but one of the things Anthropic has been working on since the very beginning of our company, we’ve had a team that focuses on trying to understand and look inside the models.

EZRA KLEIN: So I try as best I can, as somebody not well-versed in the technology here, to follow this work on what you’re describing, which I think, broadly speaking, is interpretability, right? Can we know what is happening inside the model? And over the past year, there have been some much hyped breakthroughs in interpretability.

DARIO AMODEI: I think, first, on interpretability, we are seeing substantial progress on being able to characterize, I would say, maybe the generation of models from six months ago. I think it’s not hopeless, and we do see a path. That said, I share your concern that the field is progressing very quickly relative to that.

And we’re trying to put as many resources into interpretability as possible. We’ve had one of our co-founders basically founded the field of interpretability. But also, we have to keep up with the market. So all of it’s very much a dilemma, right? Even if we stopped, then there’s all these other companies in the U.S.. And even if some law stopped all the companies in the U.S., there’s a whole world of this.

EZRA KLEIN: Let me hold for a minute on the question of the competitive dynamics because before we leave this question of the machines that bullshit. It makes me think of this podcast we did a while ago with Demis Hassabis, who’s the head of Google DeepMind, which created AlphaFold.

DARIO AMODEI: I am skeptical that we need to do something totally different. So I think today, many people have the intuition that the models are sort of eating up data that’s been gathered from the internet, code repos, whatever, and kind of spitting it out intelligently, but sort of spitting it out. And sometimes that leads to the view that the models can’t be better than the data they’re trained on or kind of can’t figure out anything that’s not in the data they’re trained on. You’re not going to get to Einstein level physics or Linus Pauling level chemistry or whatever.

EZRA KLEIN: So if you’re right and you’re going to have these models that develop this internal web of truth, I get how that model can do a lot of good. I also get how that model could do a lot of harm. And it’s not a model, not an A.I. system I’m optimistic that human beings are going to understand at a very deep level, particularly not when it is first developed. So how do you make rolling something like that out safe for humanity?

DARIO AMODEI: So late last year, we put out something called a responsible scaling plan. So the idea of that is to come up with these thresholds for an A.I. system being capable of certain things. We have what we call A.I. safety levels that in analogy to the biosafety levels, which are like, classify how dangerous a virus is and therefore what protocols you have to take to contain it, we’re currently at what we describe as A.S.L. 2.

EZRA KLEIN: To the people who say, getting on this road where we are barreling towards very powerful systems is dangerous — we shouldn’t do it at all, or we shouldn’t do it this fast — you have said, listen, if we are going to learn how to make these models safe, we have to make the models, right? The construction of the model was meant to be in service, largely, to making the model safe.

Then everybody starts making models. These very same companies start making fundamental important breakthroughs, and then they end up in a race with each other. And obviously, countries end up in a race with other countries. And so, the dynamic that has taken hold is there’s always a reason that you can justify why you have to keep going.

And that’s true, I think, also at the regulatory level, right? I mean, I do think regulators have been thoughtful about this. I think there’s been a lot of interest from members of Congress. I talked to them about this. But they’re also very concerned about the international competition. And if they weren’t, the national security people come and talk to them and say, well, we definitely cannot fall behind here.

DARIO AMODEI: Yeah, so basically, all of the things you’ve said are true at once, right? There doesn’t need to be some easy story for why we should do X or why we should do Y, right? It can be true at the same time that to do effective safety research, you need to make the larger models, and that if we don’t make models, someone less safe will. And at the same time, we can be caught in this bad dynamic at the national and international level. So I think of those as not contradictory, but just creating a difficult landscape that we have to navigate.

EZRA KLEIN: Can you say what the R.S.P.s are?

DARIO AMODEI: Responsible Scaling Plan, the thing I was talking about before. The levels of A.I. safety, and in particular, tying decisions to pause scaling to the measurement of specific dangers or the absence of the ability to show safety or the presence of certain capabilities. One way I think about it is, at the end of the day, this is ultimately an exercise in getting a coalition on board with doing something that goes against economic pressures.

EZRA KLEIN: One of the things that has been on my mind around regulation is whether or not the founding insight of Anthropic of OpenAI is even more relevant to the government, that if you are the body that is supposed to, in the end, regulate and manage the safety of societal-level technologies like artificial intelligence, do you not need to be building your own foundation models and having huge collections of research scientists and people of that nature working on them, testing them, prodding them, remaking them, in order to understand the damn thing well enough — to the extent any of us or anyone understands the damn thing well enough — to regulate it?

DARIO AMODEI: I think government directly building the models, I think that will happen in some places. It’s kind of challenging, right? Like, government has a huge amount of money, but let’s say you wanted to provision $100 billion to train a giant foundation model. The government builds it. It has to hire people under government hiring rules. There’s a lot of practical difficulties that would come with it.

EZRA KLEIN: I have now, I think, heard some version of this from the head of most of, maybe all of, the A.I. companies, in one way or another. And it has a quality to me of, Lord, grant me chastity but not yet.

And at some point, everybody is going to look up and say, this is actually too much. It is too much power. And this has to somehow be managed in some other way. And even if the C.E.O.s of the things were willing to do that, which is a very open question by the time you get there, even if they were willing to do that, the investors, the structures, the pressure around them, in a way, I think we saw a version of this — and I don’t know how much you’re going to be willing to comment on it — with the sort of OpenAI board, Sam Altman thing, where I’m very convinced that wasn’t about A.I. safety. I’ve talked to figures on both sides of that. They all sort of agree it wasn’t about A.I. safety. But there was this moment of, if you want to press the off switch, can you, if you’re the weird board created to press the off switch. And the answer was no, you can’t, right? They’ll just reconstitute it over at Microsoft.

DARIO AMODEI: Yeah, I mean, I think you’re right to be skeptical, and similarly, what I said with the previous questions of there are just these dilemmas left and right that have no easy answer. But I think I can give a little more concreteness than what you’ve pointed at, and maybe more concreteness than others have said, although I don’t know what others have said. We’re at A.S.L. 2 in our responsible scaling plan. These kinds of issues, I think they’re going to become a serious matter when we reach, say, A.S.L. 4. So that’s not a date and time. We haven’t even fully specified A.S.L. 4 —

EZRA KLEIN: Just because this is a lot of jargon, just, what do you specify A.S.L. 3 as? And then as you say, A.S.L. 4 is actually left quite undefined. So what are you implying A.S.L. 4 is?

DARIO AMODEI: A.S.L. 3 is triggered by risks related to misuse of biology and cyber technology. A.S.L. 4, we’re working on now.

EZRA KLEIN: Be specific. What do you mean? Like, what is the thing a system could do or would do that would trigger it?

DARIO AMODEI: Yes, so for example, on biology, the way we’ve defined it — and we’re still refining the test, but the way we’ve defined it is, relative to use of a Google search, there’s a substantial increase in risk as would be evaluated by, say, the national security community of misuse of biology, creation of bioweapons, that either the proliferation or spread of it is greater than it was before, or the capabilities are substantially greater than it was before.

Again, I don’t really know what it looks like. You’re right. All of these companies have investors. They have folks involved. You talk about just handing the models over. I suspect there’s some way to hand over the most dangerous or societally sensitive components or capabilities of the models without fully turning off the commercial tap. I don’t know that there’s a solution that every single actor is happy with. But again, I get to this idea of demonstrating specific risk.

EZRA KLEIN: When you imagine how many years away, just roughly, A.S.L. 3 is and how many years away A.S.L. 4 is, right, you’ve thought a lot about this exponential scaling curve. If you just had to guess, what are we talking about?

DARIO AMODEI: Yeah, I think A.S.L. 3 could easily happen this year or next year. I think A.S.L. 4 —

EZRA KLEIN: Oh, Jesus Christ.

DARIO AMODEI: No, no, I told you. I’m a believer in exponentials. I think A.S.L. 4 could happen anywhere from 2025 to 2028.

EZRA KLEIN: So that is fast.

DARIO AMODEI: Yeah, no, no, I’m truly talking about the near future here. I’m not talking about 50 years away. God grant me chastity, but not now. But “not now” doesn’t mean when I’m old and gray. I think it could be near term. I don’t know. I could be wrong. But I think it could be a near term thing.

EZRA KLEIN: But so then, if you think about this, I feel like what you’re describing, to go back to something we talked about earlier, that there’s been this step function for societal impact of A.I., the curve of the capabilities exponential, but every once in a while, something happens, ChatGPT, for instance, Midjourney with photos. And all of a sudden, a lot of people feel it. They realize what has happened and they react. They use it. They deploy it in their companies. They invest in it, whatever.

You could use coronavirus, I think, as another example where there was a significant enough global catastrophe that companies and governments and even people did things you never would have expected. But the examples we have of that happening are something terrible. All those examples end up with millions of bodies.

I’m not saying that’s going to be true for A.I., but it does sound like that is a political economy. No, you can’t imagine it now, in the same way that you couldn’t have imagined the sort of pre and post-ChatGPT world exactly, but that something happens and the world changes. Like, it’s a step function everywhere.

DARIO AMODEI: Yeah, I mean, I think my positive version of this, not to be so — to get a little bit away from the doom and gloom, is that the dangers are demonstrated in a concrete way that is really convincing, but without something actually bad happening, right? I think the worst way to learn would be for something actually bad to happen. And I’m hoping every day that doesn’t happen, and we learn bloodlessly.

EZRA KLEIN: We’ve been talking here about conceptual limits and curves, but I do want, before we end, to reground us a little bit in the physical reality, right? I think that if you’re using A.I., it can feel like this digital bits and bytes, sitting in the cloud somewhere.

DARIO AMODEI: Yeah, so one, I think this may end up being the greatest geopolitical issue of our time. And man, this relates to things that are way above my pay grade, which are military decisions about whether and how to defend Taiwan. All I can do is say what I think the implications for A.I. is. I think those implications are pretty stark. I think there’s a big question of like, OK, we built these powerful models.

EZRA KLEIN: Well, there is some insight you should have into it, which is that you’re a customer here, right? And so, five years ago, the people making these chips did not realize what the level of demand for them was going to be. I mean, what has happened to Nvidia’s stock prices is really remarkable.

DARIO AMODEI: We’ve been able to get the compute that we need for this year, I suspect also for next year as well. I think once things get to 2026, 2027, 2028, then the amount of compute gets to levels that starts to strain the capabilities of the semiconductor industry. The semiconductor industry still mostly produces C.P.U.s, right? Just the things in your laptop, not the things in the data centers that train the A.I. models. But as the economic value of the GPUs goes up and up and up because of the value of the A.I. models, that’s going to switch over.

But you know what? At some point, you hit the limits of that or you hit the limits of how fast you can switch over. And so, again, I expect there to be a big supply crunch around data centers, around chips, and around energy and power for both regulatory and physics reasons, sometime in the next few years. And that’s a risk, but it’s also an opportunity. I think it’s an opportunity to think about how the technology can be governed.

EZRA KLEIN: How about the question of energy? I mean, this requires just a tremendous amount of energy. And I mean, I’ve seen different numbers like this floating around. It very much could be in the coming years like adding a Bangladesh to the world’s energy usage. Or pick your country, right? I don’t know what exactly you all are going to be using by 2028.

DARIO AMODEI: It really comes down to, what are the uses that the model is being put to, right? So I think the worrying case would be something like crypto, right? I’m someone who’s not a believer that whatever the energy was that was used to mine the next Bitcoin, I think that was purely additive. I think that wasn’t there before. And I’m unable to think of any useful thing that’s created by that.

EZRA KLEIN: But to take the other side of it, I mean, I think the difference, when you say this is always a question we have when we’re growing G.D.P., is it’s not quite. It’s cliché because it’s true to say that the major global warming challenge right now is countries like China and India getting richer. And we want them to get richer. It is a huge human imperative, right, a moral imperative for poor people in the world to become less poor. And if that means they use more energy, then we just need to figure out how to make that work. And we don’t know of a way for that to happen without them using more energy.

DARIO AMODEI: Yeah, I mean, I think the concerns are real. Let me push back a little bit, which is, again, I don’t think the benefits are purely in the future. It kind of goes back to what I said before. Like, there may be use cases now that are net energy saving, or that to the extent that they’re not net energy saving, do so through the general mechanism of, oh, there was more demand for this thing.

EZRA KLEIN: I guess you could imagine a world where we were, somehow or another, incentivizing uses of A.I. that were yoked to some kind of social purpose. We were putting a lot more into drug discovery, or we cared a lot about things that made remote work easier, or pick your set of public goods.

DARIO AMODEI: Not everyone has the same conception of social good. One person may think social good is this ideology. Another person — we’ve seen that with some of the Gemini stuff.

EZRA KLEIN: Right.

DARIO AMODEI: But companies can try to make beneficial applications themselves, right? Like, this is why we’re working with cancer institutes. We’re hoping to partner with ministries of education in Africa, to see if we can use the models in kind of a positive way for education, rather than the way they may be used by default. So I think individual companies, individual people, can take actions to steer or bend this towards the public good.

EZRA KLEIN: But there’s also the question of training data and the intellectual property that is going into things like Claude, like GPT, like Gemini. There are a number of copyright lawsuits. You’re facing some. OpenAI is facing some. I suspect everybody is either facing them now or will face them.

And I recognize you probably can’t comment on lawsuits themselves, but I’m sure you’ve had to think a lot about this. And so, I’m curious both how you understand it as a risk, but also how you understand it morally. I mean, when you talk about the people who invent these systems gaining a lot of power, and alongside that, a lot of wealth, well, what about all the people whose work went into them such that they can create images in a million different styles?

And I mean, somebody came up with those styles. What is the responsibility back to the intellectual commons? And not just to the commons, but to the actual wages and economic prospects of the people who made all this possible?

DARIO AMODEI: I think everyone agrees the models shouldn’t be verbatim outputting copyrighted content. For things that are available on the web, for publicly available, our position — and I think there’s a strong case for it — is that the training process, again, we don’t think it’s just hoovering up content and spitting it out, or it shouldn’t be spitting it out. It’s really much more like the process of how a human learns from experiences. And so, our position that that is sufficiently transformative, and I think the law will back this up, that this is fair use.

And just as kind of we transition from an agrarian society to an industrial society and the meaning of work changed, and it was no longer true that 99 percent of people were peasants working on farms and had to find new methods of economic organization, I suspect there’s some different method of economic organization that’s going to be forced as the only possible response to disruptions to the economy that will be small at first, but will grow over time, and that we haven’t worked out what that is. We need to find something that allows people to find meaning that’s humane and that maximizes our creativity and potential and flourishing from A.I.

EZRA KLEIN: But I want to sit in between the narrow legal response and the broad “we have to completely reorganize society” response, although I think that response is actually possible over the decades. And in the middle of that is a more specific question. I mean, you could even take it from the instrumental side. There is a lot of effort now to build search products that use these systems, right? ChatGPT will use Bing to search for you.

That also, I think, in the long run, creates a training data problem, even if you just want to look at it instrumentally, where if it becomes nonviable to do journalism or to do a lot of things to create high quality information out there, the A.I.’s ability, right, the ability of all of your companies to get high quality, up-to-date, constantly updated information becomes a lot trickier. So there both seems to me to be both a moral and a self-interested dimension to this.

DARIO AMODEI: Yeah, so I think there may be business models that work for everyone, not because it’s illegitimate to train on open data from the web in a legal sense, but just because there may be business models here that kind of deliver a better product. So things I’m thinking of are like newspapers have archives. Some of them aren’t publicly available. But even if they are, it may be a better product, maybe a better experience, to, say, talk to this newspaper or talk to that newspaper.

EZRA KLEIN: Let me try to end by asking a bit about how to live on the slope of the curve you believe we are on. Do you have kids?

DARIO AMODEI: I’m married. I do not have kids.

EZRA KLEIN: So I have two kids. I have a two-year-old and a five-year-old. And particularly when I’m doing A.I. reporting, I really do sit in bed at night and think, what should I be doing here with them? What world am I trying to prepare them for? And what is needed in that world that is different from what is needed in this world, even if I believe there’s some chance — and I do believe there’s some chance — that all the things you’re saying are true. That implies a very, very, very different life for them.

DARIO AMODEI: The very short answer is, I don’t know, and I have no idea, but we have to try anyway, right? People have to raise kids, and they have to do it as best they can. An obvious recommendation is just familiarity with the technology and how it works, right? The basic paradigm of, I’m talking to systems, and systems are taking action on my behalf, obviously, as much familiarity with that as possible is, I think, helpful.

EZRA KLEIN: Possibly first.

DARIO AMODEI: Yeah, right, possibly first.

EZRA KLEIN: It seems better at coding than it is at other things.

DARIO AMODEI: I don’t think it’s going to work out for any of these systems to just do one for one what humans are going to do. I don’t really think that way. But I think it may fundamentally change industries and professions one by one in ways that are hard to predict. And so, I feel like I only have clichés here. Like get familiar with the technology. Teach your children to be adaptable, to be ready for a world that changes very quickly. I wish I had better answers, but I think that’s the best I got.

EZRA KLEIN: I agree that’s not a good answer. [LAUGHS] Let me ask that same question a bit from another direction, because one thing you just said is get familiar with the technology. And the more time I spend with the technology, the more I fear that happening. What I see when people use A.I. around me is that the obvious thing that technology does for you is automate the early parts of the creative process.

The part where you’re supposed to be reading something difficult yourself? Well, the A.I. can summarize it for you. The part where you’re supposed to sit there with a blank page and write something? Well, the A.I. can give you a first draft. And later on, you have to check it and make sure it actually did what you wanted it to do and fact-checking it. And but I believe a lot of what makes humans good at thinking comes in those parts.

And I am older and have self-discipline, and maybe this is just me hanging on to an old way of doing this, right? You could say, why use a calculator from this perspective. But my actual worry is that I’m not sure if the thing they should do is use A.I. a lot or use it a little.

This, to me, is actually a really big branching path, right? Do I want my kids learning how to use A.I. or being in a context where they’re using it a lot, or actually, do I want to protect them from it as much as I possibly could so they develop more of the capacity to read a book quietly on their own or write a first draft? I actually don’t know. I’m curious if you have a view on it.

DARIO AMODEI: I think this is part of what makes the interaction between A.I. and society complicated where it’s sometimes hard to distinguish when is an A.I. doing something, saving you labor or drudge work, versus kind of doing the interesting part. I will say that over and over again, you’ll get some technological thing, some technological system that does what you thought was the core of what you’re doing, and yet, what you’re doing turns out to have more pieces than you think it does and kind of add up to more things, right?

EZRA KLEIN: I think, then, that’s a good place to end this conversation. Though, obviously, the exponential curve continues. So always our final question — what are three books you’d recommend to the audience?

DARIO AMODEI: So, yeah, I’ve prepared three. They’re all topical, though, in some cases, indirectly so. The first one will be obvious. It’s a very long book. The physical book is very thick, but “The Making of the Atomic Bomb,” Richard Rhodes. It’s an example of technology being developed very quickly and with very broad implications. Just looking through all the characters and how they reacted to this and how people who were basically scientists gradually realized the incredible implications of the technology and how it would lead them into a world that was very different from the one they were used to.

That’s all the backdrop of it. And the core of it is about some fundamentally new technological object that is being brought into that world and how everyone reacts to it, how governments react to it, how individual people react to it, and how political ideologies react to it. And so, I don’t know. When I read that a few years ago, I saw a lot of parallels.

EZRA KLEIN: Dario Amodei, thank you very much.

EZRA KLEIN: This episode of “The Ezra Klein Show” was produced by Rollin Hu. Fact-checking by Michelle Harris. Our senior engineer is Jeff Geld. Our senior editor is Claire Gordon. The show’s production team also includes Annie Galvin, Kristin Lin and Aman Sahota. Original music by Isaac Jones. Audience strategy by Kristina Samulewski and Shannon Busta. The executive producer of New York Times Opinion Audio is Annie-Rose Strasser. Special thanks to Sonia Herrero.

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