korean essay about myself

How to Introduce Yourself in Korean

Last modified: Aug 09, 2023 | 7 min read | By Laura Toyryla

When you meet a new Korean person , knowing how to introduce yourself in Korean is very helpful ! In this way, you can easily introduce yourself and start communicating with the locals.

How you’ll introduce yourself in the Korean language will vary based on the situation, we can get you started on the basics. Add to these based on your situation. Let’s get started!

• 1.1 1. Greet them by saying 안녕하세요 (annyeonghaseyo).
• 1.2 2. Bow as you greet them.
• 1.3 3. If you shake hands, use your right hand or both hands.
• 1.4 4. Address them by their title or family name.
• 2.1 Basic Greetings in Korean
• 2.2 Introducing Your Age in Korean
• 2.3 Introducing Where You Are From in Korean
• 2.4 Introducing Your Occupation in Korean
• 3.1 List of Hobbies in Korean

Things to know when meeting new people in Korea

It’s good to go over a couple of points on the manners and culture in Korea first. This is extremely important if you’re in South Korea . After you’ve learned them, we’ll get started on how to introduce yourself in Korean ! These situations are also often portrayed on television, so you can see this in Korean drama .

1. Greet them by saying 안녕하세요 (annyeonghaseyo).

Before anything else, greet them by saying 안녕하세요 (annyeonghaseyo). It is important in Korean culture to be polite and show respect toward others. First impressions are everything.

2. Bow as you greet them.

3. If you shake hands, use your right hand or both hands.

When you shake hands, use your right hand or both hands . You can show more respect by bowing and touching your left hand to your right or grabbing your stomach with your left hand.

4. Address them by their title or family name.

Koreans do not refer to each other by their first name unless they are close friends, and even then, they often go by “언니 (eonni),” “오빠 (oppa)” and so on .

When you are meeting a new person, use their title or family name unless they say otherwise .

How to introduce yourself in Korean

Now that we’ve gone through the important things to know when meeting a new friend in South Korea, let’s proceed with learning self-introduction in Korean. We will teach basic greetings, how to state your age and where you’re from your job , and the usual conversations people have when meeting for the first time .

You’ll also get to learn more about Korean grammar and more Korean words . When you greet people in Korean, they are likely to talk to you in Korean , and a Korean self-introduction script might come in handy!

You might know how to speak Korean, but if you don’t know how to read the Korean Alphabet, you can learn it here in about 1 hour.

Basic Greetings in Korean

As you learn Korean, how to greet someone is one of the first things that you’ll know, whether it’s from recognizing phrases in a Korean drama or as part of general language learning. The Korean phrases below are essential in introducing yourself in Korean and teaching the basics of what you can use, whether the standard or formal form or way to say them.

“Hello” in Korean

The most common way to greet someone you’ve met for the first time is by saying “hello.” Below is how you can say it in a standard way.

Our article on how to say “hello” in Korean shows different ways to say this greeting.

안녕하세요 (annyeonghaseyo)

“Nice to meet you” in Korean

When meeting someone for the first time, you can say, “It’s nice to meet you.” You can say it in a standard or a formal way.

We also have a separate article that teaches you how to say “nice to meet you” in Korean in many other ways.

처음 뵙겠습니다 (cheoeum boepgetseumnida)

It’s nice to meet you!

만나서 반갑습니다 (mannaseo bangapseumnida)

Your self-introduction in Korean

When introducing yourself to someone, you can simply state your name following the sample sentence below. The example shown is can be used in formal situations.

제 이름은 존 입니다. (je ireumeun jonimnida)

My name is John.

Introducing Your Age in Korean

Age makes a big difference in your interactions in Korea . You want to use a more formal version and polite language in introductions for people in a higher social position (your boss, your elders, etc.). You can use standard versions of the Korean language when you introduce yourself in Korean to those who are the same age or younger than you.

저는 스물네 살입니다 (jeoneun seumulle sarimnida)

I’m 24 years old.

저는 스물네 살이에요 (jeoneun seumulle sarieyo)

As you tell others your age, it’s best if you’re familiar with Korean numbers, and you should use the native Korean Number System. We have a complete guide for Korean numbers here . Here’s a quick refresher if you need it.

Note that the Korean age is not the same as the international age. You can be up to two years older in Korean age.

Introducing Where You Are From in Korean

Something that will come up when you introduce yourself in Korean, your partner will probably want to know where you’re from .

저는 미국 에서 왔습니다 (jeoneun migugeseo watseumnida)

I am from the U.S .

저는 미국 에서 왔어요 (jeoneun migugeseo wasseoyo) 

I am from the U.S.

Example Countries in Korean

You can fill in the blank with the country that applies to you . For more countries, you can check our post about how to say “country” in Korean .

Introducing Your Occupation in Korean

Work is important in Korean culture , and it’s likely to come up when you introduce yourself in Korean, either during your self-introduction or as a question after you finish.

저는 학생 입니다 (jeoneun haksaengimnida)

I am a student.

저는 모델 이에요 (jeoneun moderieyo)

I am a model.

저는 가수 예요 (jeoneun gasuyeyo)

I am a singer.

Example Occupations in Korean

Here are some examples of jobs and what they are called in Korean. You can use these with the sentences above to introduce yourself in Korean as well as to explain your occupation.

Conversation topics when introducing yourself in Korean

After you introduce yourself in Korean and finish with the basic questions South Koreans will ask, you might want to introduce some other topics to keep the conversation going. Here are some basic sentences you can use.

저는 학원에서 한국어를 배웠습니다 (jeoneun hagwoneseo hangugeoreul baewotseumnida)

I learned Korean at the academy.

저는 혼자서 한국어를 공부했어요 (jeoneun honjaseo hangugeoreul gongbuhaesseoyo)

I studied Korean by myself.

저는 친구에게서 한국어를 배웠어요 (jeoneun chinguegeseo hangugeoreul baewosseoyo)

I learned Korean from my friend.

저는 서울 에서 살고 있습니다 (jeoneun seoureseo salgo itseumnida)

I live in Seoul.

저는 필리핀 에서 살고 있어요 (jeoneun pillipineseo salgo isseoyo)

I live in the Philippines.

제 취미는 러닝 이에요 (je chwimineun reoningieyo)

My hobby is running.

제 취미는 요리 예요 (je chwimineun yoriyeyo)

My hobby is cooking.

독서 는 제 취미 중 하나예요 (dokseoneun je chwimi jung hanayeyo)

Reading is one of my hobbies.

If you’d like to know more about constructing sentences in Korean, we have a full article on Korean sentence structure .

List of Hobbies in Korean

For more lists of hobbies and activities, you can check our separate article focused on hobbies in Korean and sports in Korean .

Now you know how to introduce yourself to your new Korean friends or some native speakers in Korean! If you’re looking for more, check out our fantastic resources on learning Korean here . We also have a structured online Korean language web program that will teach you how to have a 3-minute conversation in the first 90 days.

Did you find today’s lesson on learning Korean useful? Practice introducing yourself in the comments, and we’ll reply with how you did!

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155 thoughts on “How to Introduce Yourself in Korean”

i am really thankful and gratufull for you guys now i can talk korean and write it thank you so much

Awesome! You’re welcome and thanks for sharing, Lee. ^^

Thanks for this! Sir, now I can have basic conversations and write in Korean! Could you please suggest more ways to sharpen my Korean?

You’re welcome, Jack! ^^ Glad to hear you’re learning Korean. To sharpen your Korean skills, you can check out our articles Korean conversation – Practice through dialogues and Korean Practice – How to effectively use your language skills .

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How To Introduce Yourself in Korean [Complete Guide]

There is always a specific etiquette to follow when introducing yourself in any culture and foreign language. Some people place a great deal of importance on this, since first impressions are everything. However, there is a particular value placed on introductions in Korean and it’s essential to understand the nuances.

If you will be traveling to South Korea in the future, then it’s imperative you develop a solid grasp on introducing yourself. This will include learning various levels of speech, honorifics, important phrases, how to present yourself and so many others.

While it isn’t difficult in theory, there are many intricacies and details to know. Certainly, it can get overwhelming and confusing. However, consistent practice will have you memorizing it in no time.

Upon Meeting New People

The first thing you must do when you meet a new person in Korea is to address them by an appropriate speech level along with any necessary honorifics. This is because Koreans value hierarchy and the language used reflects this highly regarded practice.

Therefore, you will speak to people older and younger than you differently as well as those who are friends or your own age. This is what they refer to as levels of speech. However, the addition of honorifics intensifies this social requirement.

Levels of Speech

For elders as well as those of a higher status than you (even if they are younger or your own age), the formal is most appropriate. Almost all sentences using this speech level will end with –ㅂ니다 (b-ewe-nee-dah).

The semi-formal is for your friends, people your own age or those of an equal status. Also known as polite speech, this is the standard and often used in most everyday situations. These sentences usually end in 요 (yoh).

The informal, or casual, speech is for those younger or people who have less seniority than you have. Alternatively, you can also use it with people you know really well and to whom you’re close.

About Honorifics

Honorifics are a way Koreans show respect through speech and a display of regarding social hierarchy. Seniority and status are very important to the culture, with the language reflecting this value. The use of titles, pronouns, verbs and nouns will vary depending on the honorific.

These are a way to show others in public how close you are to the person with whom you’re speaking. The more honorifics, the further away the people are from each other. The more informal use of speech indicates they’re close. Ergo, using the wrong greeting and manner of addressing someone is a sign of disrespect and rudeness. So, you want to avoid this at all costs.

This means that when you first meet someone, it’s always good to use standard polite speech until you can get a feel for their position in contrast to yours along with age. Because honorifics are a whole section of the Korean language, we’ll only mention the ones important to know when introducing yourself.

Comparing Honorifics with Levels of Speech

Levels of speech can change and vary depending on the situation you’re in. For instance, you might use formal for speaking in front of a large audience or as a news broadcaster. You can even use various speech levels to refer to yourself depending on the topic in question.

Honorifics are specific in that they display cognizant respect toward the listener or the person about whom you’re speaking. Oftentimes, it will be a requirement when speaking to someone clearly older than you are or who has a higher social status. Unlike speech levels, you cannot use honorifics to speak about yourself.

Patience & Observation

Don’t worry, if you’re going to South Korea and you accidentally say something wrong, most Koreans are very forgiving. However, make a concerted effort never to do it again& especially with the same people.

The best way to observe introductions is to watch K-dramas. While everything else in these soap operas is outlandish and something out of fantasy, their social interactions are right on point.

About the Pronunciations

With many of the words, phrases and sentences laid out below, there is a phonetic pronunciation spelled out. These are not the official transliteration from Hangeul. While the Romanization is helpful, it doesn’t always ring true to actual annunciation.

Therefore, understand these are mere approximations to help English speakers say the words in their rightful context. If you do not find them helpful, you have all the encouragement to supplant your own.

Saying, “Hello” in Korean

Once you see someone face-to-face, you want to start by saying, “hello,” as you would in any language. The following chart details how to say this according to the appropriate speech level. The semi-formal version is the standard.

You can use the informal version to address people you know well or are close to as a way of saying “hi.” If an elder or other such higher up addresses you with the informal, they are being subtly disrespectful. This isn’t always the case, but, sometimes it can be& it will rely on the other words they use toward you after it.

Bowing & Shaking Hands

Once you say hello for the first time, bow at the hips toward them with your left hand on your stomach. Every time you see them afterwards, a head nod will do. That is of course, the person you’re speaking to is older or of a higher status. Then, you bow every time.

Remember, first you say, “hello,” then bow and finally you shake hands. You won’t always shake hands, but, when you do, you use your right hand. Only use the left if you intend to shake with both hands, where the left hand sits on the wrist, elbow or arm of the right. Other than that, leave your left hand out.

This is because the left hand has associations with death and negativity. Older people and those in formal settings could become very offended if you offer a handshake with your left.

Self-Introductions

After initial pleasantries, you should offer your name or you may hear someone ask what your name is. It looks like this: 이름이 뭐에요. You pronounce it eel-euhm-ee mwoo-eye-oh. Then, you have two ways in which to answer:

In the second sentence, notice the backslash. There is a difference in what you use depending on if your name uses a consonant for a vowel. A consonant requires 이에요 whereas a vowel uses 예요. This is because of a grammar rule that says no name in Korean can end in a consonant.

To illustrate, names like David, John, Doug, Jennifer, Lauren and Abigail will use 이에요. The other ending, 예요, is for names such as Anna, Jeanine, Laci, Alexei, Constantine or Mostafa.

Saying “Nice to Meet You”

Once you say hello to someone in Korea, much like in the West, you’ll say something along the lines of “nice to meet you.” It’s polite, courteous and shows a genuine interest in the person to whom you’re speaking. In Korean, it’s the exact same thing in principle and concept, but there are two ways of saying it. They are as follows:

Mentioning Your Age

At some point during the introduction, the person you’re speaking to may ask you what age you are. While in the West we consider it a little rude to ask someone’s age, it’s not this way in Korea. They simply want to gauge where you fit in their hierarchy. It usually looks and sounds like:

몇 살이에요? (myeo-ch sal-ee-eye-yoh?)

몇 살이세요? (myeo-ch sal-ee-say-yoh?)

The Korean number system is too long to get into here. Therefore, you should study it to get your precise age and how you use it in a sentence. But, for the sake of example, let’s say you’re 35 years old. You will respond with:

• Formal: 제 나이는 서른 다섯입니다 (pronounced chay nigh-een-euhn say-oh-leuhn thah-say-oh-sh-eeb-nee-dah)
• Semi-Formal: 저 는 서른 다섯 살이야 (pronounced chay-oh neuhn say-oh-leuhn thah-say-oh sh-al-ee-yah)

Your Home Country

Naturally, Koreans are going to notice that you’re not from around that part of the world. So, they will probably ask you where you come from or what you home nation is. They’ll ask you this in one of three ways:

You will answer with the formal 저는_____ 에서 왔습니다 (pronounced zho-neuhn _____~eseo wah-tseuhm-nee-dah) or the semi-formal 나는 _____ 에서 왔어요 (pronounced nah-neuhn _____eseo wah-soh-yoh).

If you’re from the United States:

• Formal: 저는 미국에서 왔습니다 (pronounced zho-neuhn mee-gkou-g-eseo wah-tseuhm-nee-dah)
• Semi-formal: 나는 미국에서 왔어요 (pronounced nah-neuhn mee-gkou-g-eseo wah-soh-yoh)

However, you could be from another country. While there are grammatical rules for changes, you simply replace the blank with the country name in Korean. The small list below illustrates some of them:

• Australia: 호주 (ho-choo)
• Brazil: 브라질 (beuh-lah-zeel)
• Canada: 캐나다 (kay-nah-dah)
• Egypt: 이집트 (ee-jeep-teuh)
• France: 프랑스 (peuh-lahng-seuh)
• Germany: 독일 (dog-eel)
• Great Britain: 대 브리튼 섬 (die beuh-leet-euhn say-ohm)
• Greece: 그리스 (geuh-lee-seuh)
• Ireland: 아일랜드 (ay-lend-euh)
• Mexico: 멕시코 (mek-see-koh)
• Norway: 노르웨이 (nole-deuh-way)
• Russia: 러시아 (low-see-yah)
• Scotland: 스코틀랜드 (seuh-koh-tell-an-deuh)
• Sweden: 스웨덴 (seuh-way-den)

In some introductions, you’ll want to tell the person what you do as a job or career. You will use either:

• Formal: 저는 _____ 입니다 (cheo-neuhn _____~m nee-dah)
• Semi-Formal: 저는 _____ 에요 (cheo-neuhn   _____ ~eye-oh)

You simply fill in the blank with the appropriate occupation:

• Actor: 배우 (bay-oh)
• Artist: 아티스트 (ah-tees-euh-teuh)
• Athlete: 운동 선수 (oon-dong sey-ohn-soo)
• Banker: 은행가 (euh-nhayng-gah)
• Barista: 바리 스타 (baree seuhta)
• Bartender: 바텐더 (bah-ten-doh)
• Broadcaster: 방송인 (bahng-sohng-een)
• Dancer: 춤추는 사람 (choum-chou-neuhn sah-lahm)
• Editor: 편집자 (peeone-cheeb-jah)
• Skin Esthetician: 피부미용사 (pee-boum-ee-yong-sah)
• Farmer: 농장주 (nong-chahng-choo)
• Hairdresser: 이발사 (ee-bahl-sah)
• Janitor: 관리인 (gwan-lien)
• Journalist: 기자 (gkee-jah)
• Musician: 음악가 (euhm-ah-gkah)
• Programmer: 프로그램 제작자 (peuh-low-geul-ehm jay-jahg-zah)
• Singer: 가수 (gkah-soo)
• Student: 학생 (hahg-sayng)
• Teacher: 선생님 (sohn-sayng-eem)
• Veterinarian: 수의사 (soo-wee-sah)
• Waiter: 웨이터 (way-teuh)
• Writer: 작가 (chah-gkah)

Certainly, someone may ask you about your likes and hobbies. These usually come from people you’re friends with or have some sort of closer relation, so we’ll give only the semi-formal version.

Usually they’ll ask you, “what is your hobby?” 당신의 취미는 무엇입니까? (dahng-shin-oowee chweemee-neuhn moo-ohs ee-bean-ee-kah?)

You can answer with statement such as:

As with occupation and country, fill in the blank with your preferred hobby:

• Bicycling: 자전거 타는 것 (zha-cheong-ayo tahn-neuhn gkohs)
• Bird Watching: 야조 관찰 (yah-cho gwan-chayl)
• Bowling: 볼링 (bou-ling)
• Climbing or Hiking: 등산 (deuhng-sahn)
• Cooking: 요리 (yoh-lee)
• Crafting: 공예 (goung-yay)
• Dancing: 댄스 (den-seuh)
• Gardening: 원예 (whoa-n-yay)
• Golfing: 골프 (goal-peuh)
• Listening to Music: 음악을 듣고 (euhm-agk-eul deuhd-gkho)
• Meditating: 명상(mee-yong-sang)
• Painting: 그림 (geuh-leem)
• Playing Video Games: 비디오 게임하기 (bid-ee-oh gaym-hah-gee)
• Reading: 독서 (toke-soh)
• Running: 달리기 (tahl-lee-gee)
• Sculpting: 조각 (cho-gahg)
• Singing: 명음 (mee-yong-euhm)
• Traveling: 여행 (yoh-hang)
• Walking: 걷는 (kohd-neuhn)
• Watching Movies: 영화 감상 (yong-wah kam-sahng)
• Writing: 글쓰기(geuhl-seuh-gee)

Other Topics of Conversation

As you continue speaking with people, they’ll become curious about how you learned Korean or where you live now, among other such questions. Likewise, you’ll also want to know about them, so keep the questions in mind along with the answers.

Learning Korean

• Where did you learn Korean? 한국어는 어디서 배웠어? (hangk-oog-eoh-neuhn ayo-dees-ay-oh bay-whoa-ssoh)
• Who taught you Korean?   누가 한국어를 가르쳐 줬어? (noo-gkah hang-oog-oh-leuhl gahl-euhch-yay-oh chwahss-oh?)
• I learned Korean at school. 나는 학교에서 한국어를 배웠다 (nahn-neuhn hahgk-gkoh-say-oh hang-oog-oh-leuhl baywoss-dah)
• I learned Korean from a friend. 나는 친구에게 한국어를 배웠다 (nahn-neuhn cheen-kuay-gay hang-oog-oh-leuhl bay-whoa-ss-dah)
• I studied Korean with a friend. 나는 친구와 한국어를 공부했다 (nahn-neuhn cheen-kuay hang-oog-oh-leuhl gong-boo-hay-ss-dah)
• I studied Korean in college. 나는 대학에서 한국어를 공부했다 (nahn-neuhn day-hahg-esayoh hang-oog-oh-leuhl gong-boo-hay-ss-dah)
• I studied Korean on my own. 나는 한국어를 독학으로 공부했다 (nahn-euhn hang-oog-oh-leuhl doag-hahg-euhl-owe gong-boo-hay-ss-dah)

Your Current Home

• Where do you live? 어디 살아요? (oh-dee sal-aye-oh)
• I live in Seoul. 나는 서울에 산다 (nahn-euhn sohl-eh sahn-dah)
• I live down the street. 나는 길 아래에 산다 (nahn-euhn keel ah-lay-ah sahn-dah)
• I live in Busan. 나는 부산에 산다 (nahn-euhn boo-sahn-eh sahn-dah)
• I live in Chicago. 나는 시카고에 산다 (nahn-euhn shee-cah-goh-eh sahn-dah)
• I live a few miles north. 나는 북쪽으로 몇 마일 떨어진 곳에 산다 (nahn-euhn boogk-chok-euh-low mee-och mah-eel tay-ohl-ohcheen gohs-eh sahn-dah)

Marital Status

• Are you married? 결혼하셨나요? (gyohl-hone-hah-shyohs-nigh-oh)
• Yes, I’ve been married for a decade. 예, 저는 결혼한 지 십 년이 되었습니다.  (yeh, cheo-neuhn gyol-hone-han chee seeb neon-ee doh-ee-ohs-euhb-nee-dah)
• Yes, I’m a newlywed. 네 저는 신혼입니다 (ne, cheo-neuhn seen-hone-eeb-nee-dah)
• No, I’m single. 아니, 난 하나입니다 (anee, nahn hah-nahb-nee-dah)
• No, I’m divorced. 아니요, 이혼했어요 (anee-yoh, ee-hone-hay-ss-oh-yo)

Children? Yes or No

• Do you have children? 자녀 있어요 (chan-yoh ees-oh-yo)
• Yes, I’m pregnant with my first child. 예, 첫 아이를 임신했습니다 (ye, cheos aye-leuhl eem-seen-hay-sseuhb-nee-dah)
• Yes, I have three children. 예, 저는 세 자녀가 있습니다 (ye, chonen she chan-yoh-gkah eeseuhb-nee-dah)
• No, I have no children. 아니요, 저는 자녀가 없습니다 (anee-yoh, cheonen chan-yogah ohbs-seuhb-nee-dah)

Pets? Yes or No

• Do you have any pets? 당신은 어떤 애완 동물을해야합니까? (dang-seen-euhn oh-tayohn aye-wahn dong-moul-euhl-hay-yah-hahb-nee-kah)
• Yes I have a cat. 네 저는 고양이가 있어요 (ne chonen goyang-eekah ees-oh-yo)
• Yes, I have a dog. 네 저는 개가 있어요 (ne chonen gay-gah ees-oh-yo)
• Yes, I have some fish. 네, 물고기가 좀 있어요 (ne, moul-gkogk-eegah chom ees-oh-yo)
• Yes, I have two birds. 그래 나에게는 두 마리의 새가 있다 (geuhl-aye nah-egg-en-en doo mah-lee-wee say-gah ees-dah)
• No, I don’t have a pet. 아니요, 저는 애완동물이 없습니다 (anee-yoh, chonen aye-wahn-dong-moul-ee ohbs-seuhb-nee-dah)

Addressing Other People

Of course, when you have a conversation with someone, you aren’t only going to talk about yourself. You’ll want to be able to address other people and ask them similar questions. Here is where speech levels and honorifics truly come into play.

First, you’ll never address someone as “you” or by their first name, especially upon first meeting. You will always employ the appropriate honorific to address an individual based on their age and social position.

However, honorifics are a huge topic that includes verbs and nouns. Newcomers should begin learning these from the start. But, for the sake of this discussion, you address them by their family role, company title or you add a suffix to their name or title. The titles below are the most common:

There are several characters added at the end of names and titles to indicate additional honorifics if not already attached to the person when addressing them. You simply add them at the end of any name or word such as “driver” or “doorman” and etc.

For the last example above, the difference will depend on the presence of a consonant or a vowel. If the name has a vowel, you will use 수야 (soo-yah). When a consonant is at the end of their name, you will use 민아 (meena). 

While each section here has its own grammatical rules, this is the basic overview of how to introduce yourself in Korean. Therefore, it’s imperative you familiarize yourself with as many nouns, verbs and numbers as you can to be able to speak accurately about yourself.

Also, always remember there’s a formal, semi-formal and, sometimes, informal way to speak to someone based on their age and social seniority. However, using the semi-formal will be the most common in your dealings with the public and meeting new people.

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How to Introduce Yourself in Korean: The 15-minute Guide

Becoming completely fluent in 국어 (the Korean language) can take years.

But if you only want to learn how to introduce yourself in Korean, all you need is 15 minutes!

In this article, I’ll teach you how to say “Hello, my name is…” and other useful phrases for when you first meet somebody. I’ll also provide you with a sample conversation and websites or apps you can use to help solidify what you’ve learned.

How to Introduce Yourself in Korean: An Overview

Basic phrases for introducing yourself in korean, “hello, my name is…”, “nice to meet you”, “i’m from…”, talking about your age, talking about your job, sample conversation for introducing yourself in korean, online resources for practicing your introducing yourself in korean, watch authentic korean videos on fluentu, connect with native speakers on hellotalk, run grammar drills on how to study korean, work on memorization with korean flashcards from quizlet, chat with a robot on mondlyar.

Download: This blog post is available as a convenient and portable PDF that you can take anywhere. Click here to get a copy. (Download)

In English, when you first meet somebody, you probably say some variation of “Hello, my name is…” You can use the same phrase in English no matter who you’re talking to.

In Korean, you need to change the phrase slightly depending on who you’re speaking with.  The way you speak to older members of the family , for example, will differ from the way you speak to close friends.

In case you’re not familiar with the different levels of formality in Korean , here’s a quick guide.

• 반말 (Casual language): Used with people younger than you and close friends. 
• 존댓말 (Polite language): Used with strangers and people you don’t know well. It comes in two forms: informal and formal. Sentences written in the informal version usually end with a “ 요 ” on the verb. Sentences written in the formal version often end in (ㅡ) ㅂ니다 .

For our purposes, we’re going to focus on the polite informal way of speaking . You can use this form in most day-to-day situations.

Just like in English, there are several ways to introduce yourself in Korean .

When you’re meeting somebody for the first time, you’ll use one of the following phrases to say “Hello, my name is…”

If your name ends in a vowel, say:

안녕하세요? 저는 _____ 예요 . Hello, my name is _____.

If your name ends in a consonant, say:

안녕하세요? 저는 _____ 이에요 . Hello, my name is _____.

You can also use one of the following phrases with no difference in meaning.

If your name ends in a vowel:

제 이름은 _____ 예요 . My name is _____.

And if your name ends in a consonant:

제 이름은 _____ 이에요 . My name is _____.

If you want to ask somebody else what their name is, you can say:

이름이 뭐예요? What is your name?

There are several ways to say “nice to meet you” in Korean, but one of the simplest is:

만나서 반가워요. Nice to meet you.

Shortly after meeting somebody, they might ask you about your background. You can use this phrase to tell somebody what country or city you’re from:

저는 _____ 에서 왔어요 . I come from _____.

Korean speakers often drop the word “I” from sentences. Usually, when you can guess the subject of the sentence from the context, you can drop 저는 without changing the meaning.

Here’s a list of some countries you might be from. Most countries sound similar to their English counterpart, but some others (like America, Australia and England) are different.

Canada: 캐나다 England: 영국 America: 미국 Ireland: 아일랜드 Scotland: 스코틀랜드 Northern Ireland: 북아일랜드 New Zealand: 뉴질랜드 India: 인도 Australia: 호주 The Philippines: 필리핀 Jamaica: 자메이카 The Bahamas: 바하마 제도

When meeting somebody for the first time, you may want to ask them how old they are so you know how formal you should be when speaking.

Saying your age is easy. You can just give a number followed by this phrase:

_____ 살이에요 . I am _____ years old.

Keep in mind that there are two different number systems in Korean. For your age, you should use native Korean numbers. If you need to brush up on your Korean numbers, here’s a reminder.

One: 하나 ( 한 ) Two: 둘 ( 두 ) Three: 셋 ( 세 ) Four: 넷 ( 네 ) Five: 다섯 Six: 여섯 Seven: 일곱 Eight: 여덟 Nine: 아홉 Ten: 열 Twenty: 스물 ( 스무 ) Thirty: 서른 Forty: 마흔 Fifty: 쉰 Sixty: 예순 Seventy: 일흔 Eighty: 여든 Ninety: 아흔

If you want to ask someone how old they are, you can use either of the following expressions, depending on whether the context is formal or casual.

나이가 어떻게 되세요? How old are you? (Formal)

몇 살이에요? How old are you? (Casual)

If you’re asking somebody older than you their age, you should say:

연세가 어떻게 되세요? How old are you?

If you want to tell somebody what you do for work, you can use the following expression, putting your job title in the blank:

저는 _____ 이에요/예요 . I am a _____.

Here’s a list of some common job titles.

Teacher: 선생님 Student: 학생 Doctor: 의사 Nurse: 간호사 Scientist: 과학자 Athlete: 운동 선수

If you want to ask somebody what their job is, you can use the following expression. It loosely translates to “what is your profession?”

무슨 일 하세요? What’s your job?

Congratulations—you now have a solid foundation for introducing yourself and striking up a conversation in Korean!

Now that you’ve learned some basic phrases , here’s an example of a conversation putting them together:

Ryan: 안녕하세요? 저는 Ryan이에요. 이름이 뭐예요? Hello, I’m Ryan. What’s your name?

Min-Soo: 저는 Min-Soo예요. 한국에서 왔어요. I’m Min-Soo. I’m from Korea.

Ryan: 만나서 반가워요. Nice to meet you.

Min-Soo: 저도요. Ryan, 나이가 어떻게 되세요? Same to you. How old are you, Ryan?

Ryan: 25살이에요. 무슨 일 하세요? I’m 25 years old. What’s your job?

Min-Soo: 학생이에요. I’m a student.

This is just one example of how this conversation might go. To really get the ball rolling, call up a classmate, conversation partner or Korean-speaking friend and run this dialogue several different times with variations in how the two characters respond. Record yourself and then listen back for extra pronunciation practice.

Merely learning these phrases once isn’t enough. You have to make time to practice!

Here are a few apps and websites to help you reinforce what you’ve learned here:

You can look up all of the phrases above to see them in action in real Korean-language media.

HelloTalk is a social media app that connects you with native speakers interested in learning your language.

You can chat, ask questions and make corrections to each other’s speech and writing. This app is great for practicing introductions, because you’ll have to introduce yourself every time you connect with someone new!

If you want to know more about this app, read our HelloTalk review .

If you want to improve your grammar and develop your fluency, How to Study Korean is one of the best free resources available on the internet.

The site has more than 150 lessons to help you improve your Korean language ability, plus games and activities to help you practice.

If you prefer your flashcards the old-fashioned way, that’s great too: simply make some using paper or notecards. Writing these phrases down will help you practice spelling and aid in memorization as well.

MondlyAR—part of the broader Mondly language-learning service—provides a virtual reality chatbox feature that can help you practice phrases you’ve learned.

Try starting a conversation and practicing the phrases you’ve learned today. Using a chatbot can be a great way to build confidence before reaching out to an actual native speaker. You can learn more about whether MondlyAR is the app for you via our review .

Other than the above, there are plenty of apps you can check out to level up your Korean.

For more information on how to introduce yourself in Korean, you can also watch this YouTube video:

And if you want to get your honorifics right, this video can help you out:

See? 15 minutes of your time, and you’re already a more confident Korean speaker.

Now that you know some basic phrases, you can use them to kickstart your next conversation in Korean. This just might be the perfect moment to transform that Korean-speaking stranger into a new friend.

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Basic self-introduction in Korean - phrases, words, and tips

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Self-introduction in your first language is easy, but doing it in a foreign language can be daunting. If you just started learning Korean, this post is perfect for you because today we will be learning how to do a self-introduction in Korean.

I will be teaching you some basic phrases and words, along with some tips that will make every Korean you meet say, “한국어를 잘 하시네요!” (hangugoreul jal hasineyo) i.e. “You speak Korean well!”

Considering that we usually do self-introductions when meeting new people, we will be using mostly polite or formal Korean speech. You might know, it’s very important to get the speech levels in Korean correct. We don’t want to be disrespectful during our first interactions, right?

I’ve made this summary chart here so you can get what you’re looking for. Read on to get all the details, and context for each expression.

First Greeting

If you are meeting someone for the first time, don’t forget your first greeting before you start introducing yourself. In polite Korean, you can say:

안녕하세요? (annyonghaseyo)

which literally translates to, “Have you been well?” This is the Korean equivalent to our standard ‘hello’ greeting. If you happen to be in a formal situation, you can say:

안녕하십니까? (annyonghasimnikka)

In Korea, it is a common gesture to bow when greeting each other. You don’t have to do a full-on 90 degrees bow; that’s usually too much for most situations. Just a gentle 15 degrees bow or a slight nod of the head should suffice.

Also, don’t be surprised if someone extends their hand for a handshake. Just remember to shake with your right hand and your left hand supporting underneath, as if you are offering your hand to the other person. Add a bow for an extra dose of respect.

In return, the person you are greeting will probably greet you the same way as you did.

If you want to learn other ways of saying ‘hello’ in Korean, you can refer to this post here .

“Glad to meet you!”

After your first greeting, you may want to say something like, “Glad to meet you,” before you introduce your name. While that is fine, I usually skip this part. But if you really mean it, you can say it by all means.

If you are meeting someone in person, you can say:

만나서 반가워요. (mannaso bangawoyo) Pleased to meet you. (polite)

만나서 반갑습니다. (choeum bwepkketsseumnida) Pleased to meet you. (formal)

You can use the above two phrases on someone you already know since both phrases express gladness in being able to meet that person, whether for the first time or not.

You may also have heard of another Korean phrase:

처음 뵙겠습니다. (choeum bwepkketsseumnida) Pleased to meet you for the first time. (formal)

You should use the above phrase for formal settings, like during business meetings etc. Also, 처음 (choeum) means ‘first time’ so it is used when meeting someone for the very first time.

“What is your name?”

This is when your self-introduction really starts: introducing your name. Before we learn how to introduce our names in Korean, it is equally important to learn the phrase, “What is your name?” in Korean to make things more conversational.

이름이 어떻게 되세요? (ireumi ottoke dweseyo) What is your name? (polite)

성함이 어떻게 되세요? (songhami ottoke dweseyo) What is your name? (formal)

The two phrases above may be a bit much to remember. If you cannot remember them well, then just remember these two key words: 이름 (ireum) and 성함 (songham). Both words mean ‘name’, just that 성함 (songham) is the honorific form of 이름 (ireum).

So if you hear any of the two keywords being mentioned as part of a question directed to you, the likelihood of them asking for your name is high. And to that question, you can answer:

제 이름은 ____예요/이에요. (je ireumeun ____yeyo/ieyo) My name is____. (polite)

제 이름은 ____입니다. (je ireumeun jeieyo) My name is____. (formal)

All you need to do now is to fill in the blank with your name. So, if I were to introduce myself to you, I would say:

제 이름은 제이예요. (je ireumeun jeieyo) My name is Jay. (polite)

제 이름은 제이입니다. (je ireumeun jeiimnida) My name is Jay. (formal)

You have gone from memorising phrases to being able to form personalised sentences on your own. Great job! The person that you have been introducing yourself to should be pretty impressed by now. That person may say something like this to you in return:

한국어를 잘 하시네요! (hangugoreul jal hasineyo) You speak Korean well!

Yes, it’s the phrase that you saw in the introduction of this post and yes, you will hear this phrase a lot if you can speak Korean (even if it’s just saying ‘hello’ and your name in Korean).

Koreans appreciate it when non-Koreans take the effort to learn their language. They are not stingy with their praises so if someone praises your Korean language skills, accept humbly and say a simple ‘thank you’ in Korean .

“How old are you?”

Some say that age is a sensitive topic so many approach it with caution. But in Korean culture, it is actually important to know someone’s age because it will determine whether you will need to use honorifics when speaking with that person.

So, you may be asked:

나이가 어떻게 되세요? (naiga ottoke dweseyo) How old are you? (polite)

몇 살이세요? (myot sariseyo) How old are you? (polite)

Let’s approach these two phrases the same way as before when we were learning how to ask for a person’s name. If you cannot remember them well, then just remember these two keywords: 나이 (nai) and 살 (sal)

So if you hear any of the two keywords being mentioned as part of question directed to you when you are doing your self-introduction, you can pretty much get ready to reply with your age:

저는 ____살이에요. (joneun ____sarieyo) I am ____ years old. (polite)

저는 ____살입니다. (joneun ____sarimnida) I am ____ years old. (formal)

Now, you will need to fill in the blank with your age. To do that, you will need to know Native Korean Numbers. To really master it, you will need to do some memorisation but to keep things simple, you just need to remember your own age.

Let’s learn how to use the table above to find out our age in Native Korean Numbers. Since we will be using the numbers with the word 살 (sal) which means ‘years old’, you will need to look at the rightmost column as reference.

E.g. If you are ‘20 years old’, then you are 스무 살 (seumu sal).

Let’s go up higher up the age table. If you are ‘25 years old’, then you are ‘twenty-five years old’ in English. Now, translate each word directly to Korean and you are 스물다섯 살 (soreundasot sal). Take note though, that only 20 is pronounced as 스무 (seumu).

If your age has a 1, 2, 3, or 4 in the ones place, you will need to say 한 (han), 두 (du), 세 (se), or 네 (ne) before the word 살 (sal).

E.g. 서른한 살 (soreunhan sal) - 31 years old

So, putting everything we have learnt together, if you are 45 years old, how can you say, “I am 45 years old,” in Korean?

저는 마흔다섯 살이에요. (joneun maheundasot sarieyo) I am 45 years old. (polite)

저는 마흔다섯 살입니다. (joneun maheundasot sarimnida) I am 45 years old. (formal)

Once you have introduced your age and you managed to find out the age of the person you are talking to, you may need to use Korean honorific titles if the other person is older than you .

That’s all for age! Let’s move on and talk about which country you are from.

“Which country are you from?”

For this part, there are different ways to say which country you are from. Before we cover that, let’s learn, “Which country are you from?” in Korean first.

There are two ways that you may be asked the question. A person may ask you where you are from:

어디에서 왔어요? (odieso wassoyo) Where are you from? (polite)

To this question, the reply is actually very simple. Since the word 어디 (odi) means ‘where’, all you have to do is to replace 어디 (odi) with the country you are from. If you are from America, you can say:

미국에서 왔어요. (migugeso wassoyo) I am from America. (polite)

미국에서 왔습니다. (migugeso watsseumnida) I am from America. (formal)

Take note that you should say the above phrase only if you are outside of the country that you say you are from.

Another way you may be asked is when the person asks you about your nationality:

어느나라 사람이에요? (oneunara saramieyo)

which literally translates to, “Which country person are you?”

어느 (oneu) means ‘which’ and 나라 (nara) means ‘country’, so to reply, all you need to do is to replace 어느나라 (oneunara) with the country you are from. Assuming you are from America, you can say:

미국 사람이에요. (miguk saramieyo) I am American. (polite)

미국 사람입니다. (miguk saramimnida) I am American. (formal)

Of the two ways to introduce where you are from, the latter gives you more flexibility since it can be used in almost all kinds of situations, regardless of where you are located. You can use it while texting as well.

Here are some countries and their names in Korean:

캐나다 (kaenada) - Canada

필리핀 (pilripin) - Philippines

인도 (indo) - India

인도네시아 (indonesia) - Indonesia

스페인 (seupein) - Spain

싱가포르 (singgaporeu) - Singapore

베트남 (beteunam) - Vietnam

미국 (miguk) - United States

브라질 (beurajil) - Brazil

터키 (toki) - Turkey

이집트 (ijipteu) - Egypt

이란 (iran) - Iran

한국 (hanguk) - South Korea

중국 (jungguk) - China

“What is your occupation?”

When being asked about your occupation, you may be asked:

직업이 뭐예요? (jigobi mwoeyo) What is your occupation? (polite)

무슨 일 하세요? (museun il haseyo) What kind of work do you do? (polite)

In the examples above, the two keywords to listen out for are 직업 (jigob) which means ‘occupation’ and 일 (il) which means ‘work’.

To that, you can reply:

저는 ____예요/이에요. (joneun yeyoieyo) I am a ____. (polite)

저는 ____입니다. (joneun imnida) I am a ____. (formal)

and fill in the blank with the name of your occupation. If you work in an office as an employee, you can say:

저는 회사원이에요. (joneun hwesawonieyo) I am an office worker. (polite)

저는 회사원입니다. (joneun hwesawonimnida) I am an office worker. (formal)

Here are some examples of different occupations in Korean:

배우 (baeu) - actor

조종사 (jojongsa) - pilot

요리사 (yorisa) - chef

간호사 (ganhosa) - nurse

군인 (gunin) - soldier

변호사 (byonhosa) - attorney

의사 (uisa) - doctor

선생님 (sonsaengnim) - teacher

학생 (haksseung) - Student

Let’s say you work at Samsung. You are really proud of it and would like to share it as part of your self-introduction. You can say:

저는 삼성에 다녀요. (joneun samsonge danyoyo) I work at Samsung. (polite)

저는 삼성에 다닙니다. (joneun samsonge danimnida) I work at Samsung. (formal)

As with other examples, you can simply replace 삼성 (samsong) with any other company name or place to fit your self-introduction.

“I like K-dramas.”

One thing that you can do to add more personality to your self-introduction is to talk about what you like. If you like television dramas, you can say:

드라마를 좋아해요. (deuramareul joahaeyo) I like dramas. (polite)

드라마를 좋아합니다. (deuramareul joahamnida) I like dramas. (formal)

In the example above, 드라마 (deurama) means drama. You can replace the word with anything else that you like and it will work just as well. Just take note if the word ends with a consonant (e.g. 게임 kkeim - game), you need to use 을 (eul) instead of 를 (reul).

E.g. 게임을 좋아해요. (kkeimeul joahaeyo) I like games. (polite)

Up to this point, you should be able to give a self-introduction of your name, age, nationality, occupation, and even talk about things that you like. That is a big step toward being able to make many new Korean friends.

Yes, it may feel scary to go up to someone new and introduce yourself in a foreign language, but I promise you that it will only get easier with practice .

Would you like to help others perfect their self-introduction skills? Sign up as a Hilokal trainer here .

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• , February 7, 2024

An Effective #1 Guide To Introduce Yourself In Korean

Imagine yourself walking alone on Nami Island, then suddenly, someone said “제 이름은 …. 입니다 Je ireumeun Imnida.”. Then, you might want to learn how to introduce yourself in Korean. Wouldn’t it be to find a new friend in the beautiful country of Korea?  Yes , this might sound as cheesy as what’s in Korean dramas, but it is not impossible to meet someone in Korea while having a good time.

Koreans are known to be polite and respectful . It is easily seen in the way they speak to other people, even strangers. Introducing yourself is one of the basic social etiquette during first meetings, and Koreans do this in the most polite way possible. So, let’s discover how to introduce yourself in Korean. Surely, after this language lesson, you will be able to make some new friends –친구 chingu .

Why Must You Learn To Introduce Yourself?

A self-introduction is not always too easy to do for some people. Some people feel anxious and shy, but some people are outgoing and want to meet many people. People can’t easily be confident in introducing their names, while some can get along easily and love to widen their circles. So, why must you learn to introduce yourself?

For starters, it breaks the ice between strangers. It saves you from awkward first meeting moments when you don’t have anything to talk about because you do not know each other. It can make other people feel confident to express themselves and share what they want to share.

Introducing yourself in business or the workplace is a requirement, especially if you’re a new employee or have just met your business partner. It is a sign of respect to the seniors and to the person you have a business with.

Lastly, it can show that you can confidently meet other people. Giving the impression that you’re okay with new friends, ideas, perspectives, and culture is one of the characteristics people love. You know that we will encounter people with different perspectives, ideas, beliefs, and traditions in our lives. If you want to gain more friends and connections, you should learn how to introduce yourself effectively.

How To Introduce Yourself In Korean?

Now that we have learned about the importance of introducing yourself, let’s learn how to introduce yourself in Korean.

The Korean language is not just a language spoken in Korea. It is also a reflection of their culture and traditions. Their politeness and respect for others can really be seen in the way they talk to others. If this is your first time learning about Korea, you should know that Koreans have different levels of politeness. These levels are determined by the age (age in Korean) and the social hierarchy of the person you are talking to.

Koreans have a formal form of spoken language, talking with elders, people in a much higher social hierarchy, and strangers.

The informal form is used when talking to people that are the same age or younger than you. It can also be your close friends or family.

The last one is the standard form. You can use this in most situations. It’s not too formal and not too informal as well. The politeness level is one of the first things you remember when you are learning Korean. Therefore, it would help if you first learned how to be respectful and polite, which could be the best first step to introducing yourself in Korean. Let’s start learning the words and phrases that you can use when introducing yourself in Korean.

1. Start With A Greeting

안녕하세요  (annyeonghaseyo).

Do you remember how Sung Deok Sun in “Reply 1988” introduces herself as a picket girl in the Olympics? Although the setting of this series is 1988, Koreans still introduce themselves that way.

“Annyeonghaseyo” or “Annyeong” is probably one of the most common Korean words that you’ll hear in Kdramas. So, why not start your self-introduction with a greeting? Just like in other cultures, we usually say ‘hello’ first before we talk to somebody. Greetings in Korean are usually done before you introduce your name in Korean. They are one of the most common icebreakers, and it also implies courtesy. This is the reason why this is the first step to introduce yourself in Korean.

In the past blog, you have learned Korean greetings . You should remember that it is not done casually like in other cultures. Koreans have their own way of doing it. They bow their heads according to the age of the person they are greeting. For handshakes, they do it with the right hand or both hands. To address them, they use their title or family name.

Here are other greetings that you can use:

2. Say Your Name

이름이 뭐에요 ireumi mwoeyo – what’s your name, 제 이름은 민준입니다. (je ireumeun minjunimnida.) – my name is minjun., format:  제 이름은 …. 입니다 (je ireumeun __ imnida) – my name is __..

In the Korean drama “The King: Eternal Monarch,” Lee Gon (the king) tells Jeong Tae-Eul his name regardless of the rule that nobody should ever know the king’s true name. This scene truly proves that there’s no rule in love.

But, of course, you’re not a king in Korea, right? So, you still have to introduce your name in Korean. This is the most important step in introducing yourself to Korean. Some people want to use their Korean names in Hangul when they go to Korea. But, for those who are just starting yet, you can use the original pronunciation of your name.

Here are some Korean names:

3. mention your age,  몇 살이세요 (myeot sariseyo) –  how old are you,  저는  스물네  살입니다 (jeoneun seumulle sarimnida) – i’m 24 years old, format: 저는 __살입니다  (jeoneun __ sarimnida) – i am__years old..

One of the most iconic Kdrama is “Goblin.” It is about a 900-year-old Goblin who is cursed to live forever until he finds his bride. Can you imagine that? The Goblin is 90 years old. Of course, it cannot happen in real life, but if you live that long, you’ll meet many people.

Koreans have different ways of counting their age before. It is not the same as what most of the culture does. They have this thing called the Korean age, which is their own way of calculating their age. Korean age is always a year or two years older than their international age because they count their age while they are in the womb; that’s why when they are born, they are already one year old. Also. Every Korean age one year during New Year’s Day.

But if you’re not Korean, you don’t have to convert your age, especially if you’re just there for vacation. Use the format above to introduce yourself age in Korean. Remember that age is important in Koreans because this will be their basis for how to address someone. If you want to learn Korean numbers, here’s a list for you. You can visit the Ling app  if you want to know more.

Here is a list of ages in Korean:

4. State Your Occupation

무슨 일 하세요 (museun il haseyo) – what’s your occupation, 교사입니다. (gyosaimnida.) – i’m a teacher., format: 저는__입니다  (jeoneun  __ imnida) – i am a __..

The next step to introduce yourself in Korean is to state your occupation. Then, share what you do for a living. Having a stable job nowadays is something that you should be proud of in your life. You are lucky if you find a job where you can do what you are passionate about.

The word for occupation in Korean is (직업 jigeop). Suppose you want to draw some inspiration on what occupation, profession, or job that you want in Korea. In that case, you might want to check Kdramas like Law School for those who want to be lawyers, Hospital Playlist, and Dr. Romantic for those who want to be in the medical field, and Itaewon Class and Start-Up if you want to start a business and start-ups.

Here is a list of occupations in Korean:

5. Mention Where You Live

어디에 사세요 (eodie saseyo) – where do you live, 저는 서울에서 살고 있습니다 (jeoneun seoureseo salgo itseumnida) – i live in seoul, format: 저는 __에서 살고 있습니다  (jeoneun __ eseo salgo isseumnida) – i live in__. .

If you are currently living in Korea, you can easily say where you live. There are many beautiful places to live in Korea that are near many tourist attractions and public transportation. You can also choose to live in places near your favorite Kdrama shooting spot, like Deoksugung Palace Stonewall Walkway from Goblin and Cheonggyecheon Stream from Vicenzo.

You can check out the list below for some examples of these beautiful cities.

Here is a list of cities in Korea:

6. State What Country You Came From

어디 출신이세요 eodi chulsiniseyo – where are you from, 저는 미국에서 왔습니다 (jeoneun migugeseo wasseumnida) – i am from u.s., format: 저는__에서 왔습니다 (jeoneun__eseo wasseumnida).

Another step to introduce yourself in Korean is Stating the country where you came from. If you are a foreigner and you just want to explore the culture of Korea. You can also use the standard form 저는 __ 에서 왔어요  (jeoneun __eseo wasseoyo) so that it would not be too formal and casual either.

Sharing the country where you came from is a conversation piece. You can share something about your culture with other people and be proud of it. This will widen both of your knowledge of different cultures.

Here is a list of countries in Korean:

7. Share Your Hobbies

당신의 취미는 무엇입니까 (dangsin-ui chwimineun mueos-ibnikka), – what are your hobbies,  제 취미는  요리 예요 (je chwimineun yoriyeyo) – my hobby is cookin g, format: 제 취미는 __이에요/예요 (je chwimineun __ieyo/yeyo)- my hobby __..

The last step that you can do to introduce yourself in Korean effectively is to share your hobbies. Of course, this is a good conversation piece, but sharing hobbies can open doors to meeting new friends with the same likes and hobbies.

There are lots of common hobbies among Koreans that you might also enjoy, like listening and dancing to the songs of their favorite Korean boy band or girl group, traveling to different parts of Korea, and of course, eating Samgyupsal with your friends.

Saying this when you introduce yourself in Korean is an interesting thing to do. Who knows? Maybe you can turn that hobby into a profession or career.  Yes , it is possible. If you want inspiration, watch “Record of Youth” Kdrama.

Here is a list of hobbies in the Korean language:

After Introducing Yourself In Korean, What’s Next?

Introducing yourself in Korean is just a step toward discovering more about Korean culture. But if you want to go further, you can try the Ling app and learn Korean more. We all know that language learning has many advantages, so why not start your free lessons now with the Ling app ? Just search it online, and in just a matter of time, you will surely learn how to read, write, and speak Korean.

So, what are you waiting for? Grab your phone and download the Ling app on the Play Store or App Store . Start learning Korean now!

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How do I introduce myself in Korean?

Introduction.

Introduce the topic of the article and explain why it is important to learn how to introduce oneself in Korean.

The Basics of Korean Introductions

Explain the basic structure of a Korean introduction, including the greeting and the self-introduction.

Formal vs. Informal Introductions

Describe the differences between formal and informal introductions in Korean, and when each should be used.

Vocabulary for Introducing Yourself in Korean

Provide a list of essential vocabulary words and phrases for introducing oneself in Korean, including common greetings, names, and occupations.

Pronunciation Tips for Korean Introductions

Offer tips for proper pronunciation when introducing oneself in Korean, including common mispronunciations to avoid.

Cultural Considerations When Introducing Yourself in Korean

Explain how cultural norms and customs influence introductions in Korea, including appropriate levels of politeness and respect.

Common Mistakes to Avoid When Introducing Yourself in Korean

List common mistakes that non-native speakers make when introducing themselves in Korean, such as using incorrect grammar or vocabulary.

Sample Dialogues for Introducing Yourself in Korean

Provide several sample dialogues that illustrate how to introduce oneself in different situations, such as at work or at a social event.

Practice Exercises for Introducing Yourself in Korean

Offer practice exercises to help learners improve their proficiency at introducing themselves in Korean, such as role-playing exercises or writing practice.

Additional Resources for Learning Korean Introductions

Provide a list of additional resources for learners who want to improve their skills at introducing themselves in Korean, such as online tutorials or language exchange programs.

Summarize the key points of the article and reinforce the importance of learning how to introduce oneself in Korean.

Include a list of references cited in the article, such as books or articles about Korean language and culture.

How can I introduce myself in Korean naturally?

To introduce yourself in Korean, say “je ireumeun” followed by your preferred name and then “imnida”. You can use your first or last name, but keep in mind that Koreans usually refer to each other by their surnames unless they have a close relationship.

What does Imnida mean?

“To be” in Korean can be expressed as the combination of the words “sal” (age) and “imnida” (to be). For instance, a person who is 21 years old would say “저는 스물한살입니다.”

How do you say my name in Korean introduction?

I go by the name of (name). This is a polite and formal way of introducing oneself and means that the speaker is sharing their name.

What is a typical Korean introduction?

In Korean, there are two common ways to introduce oneself depending on the context, formal or semi-formal. The phrase “Annyeonghaseyo” is commonly used in both situations, while “cheoeum boepgesseumnida” is reserved for more formal occasions and translates to “see you for the first time”.

How do Koreans greet themselves?

When Koreans meet someone, they say “안녕하세요 [an nyeong ha seyo]” while bowing slightly. This greeting can be used to say “Hello,” “Hi,” or “Good morning/afternoon/evening.”

What is Animida in Korean?

The word “anida” translates to “not” and is modified to “anieyo” or “animnida” when used in formal speech. When used informally, “anida” becomes “aniya.”

Strategies for Successful Korean Introductions

Apart from the vocabulary and grammar, it is essential to have an appropriate attitude when introducing yourself in Korean. One of the best strategies is to show interest in the person you are meeting. Koreans value social harmony and respect, so asking about their well-being and showing interest in their culture will make a great impression. Also, it is crucial to maintain eye contact and use appropriate body language to convey friendliness and respect.

Introducing Yourself in Business Situations

Introducing yourself in business situations in Korea requires a higher level of formality than casual settings. It is essential to address the person with their title and last name. It is also appropriate to bow slightly while introducing yourself. Using formal language shows respect and demonstrates your understanding of Korean cultural norms.

Introducing Yourself in Social Settings

In social settings, introductions can be less formal, but it is still essential to show respect. Using casual language and speaking informally can be acceptable, but it is better to err on the side of caution and start with formal language until the other person invites a more casual tone. In social settings, Koreans often ask about your age, which helps establish hierarchy within a group.

Using Honorifics and Titles

Titles such as “oppa” or “unnie” are used to describe someone older than you or someone you are close to. The use of these titles shows respect and acknowledges the social hierarchy in Korean culture. Honorifics such as “-nim” or “-ssi” are added to names based on the relationship between people, and they demonstrate respect for that person.

Learning how to introduce yourself in Korean is an essential step towards building relationships with Koreans, both socially and professionally. Understanding the cultural norms surrounding introductions, using the appropriate vocabulary and grammar, and showing respect and interest are key to successful introductions. With practice and an open mind, anyone can master the art of introducing themselves in Korean.

Related posts:

• What is South Korea popular for?
• What is a normal Korean dinner?
• What is the most common job in South Korea?
• Can foreigners open a bank account in South Korea?

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Koreabridge

How to Introduce Yourself in Korean

Whenever you meet a new Korean person, you’ll want to introduce yourself and at least the basics about you. So learning how to introduce yourself in Korean is one of the most important Korean lessons to learn!

How you’ll introduce yourself in Korean will vary based on the situation, we can get you started on the basics with formal and standard Korean, and you can add on your own additions as needed. Let’s start with those phrases!

Manners and Culture in Korea for Meeting New People in Korea

It’s good to go over a couple of points on the manners and culture in Korea before getting into introductions. After you’ve learned them, we’ll get started on explaining who you are in Korean!

1. Before anything else, greet them by saying 안녕하세요.

It is important in Korean culture to be polite and show your respect towards others. First impressions are everything.

2. Bow as you greet them.

3. If you shake hands, use your right hand or both hands.

Shaking hands isn’t awfully common in Korea outside of business, but they do sometimes take place in introductions. Use your right hand or both hands. You can show more respect by bowing and touching your left hand to your right or grabbing your stomach with your left hand.

4. It’s best to avoid direct eye contact with someone of higher authority in the beginning.

It can be considered rude to stare at someone in the eyes when you have only just met, make some eye contact but don’t stare them down.

5. Address them by their title or family name.

Koreans do not refer to each other by their first name unless they are close and even then they often go by ‘언니’, ‘오빠’ and so on . When you are meeting a new person use their title or family name. For a new friend, it may be okay just adding the 씨 (sshi) honorific to the name, you can always ask during self-introductions.

Basic Greetings in Korean

An essential part of any introduction is the greeting! These are the basics of what you can use, in both the formal and standard ways of saying them.

안녕하세요 (annyeong haseyo)

“It’s nice to meet you!” =

처음 뵙겠습니다 (cheoeum bwepkesseumnida)

만나서 반갑습니다 (mannaseo bangapseumnida)

“My name is…” =

제이름은 ______입니다 (je ireumeun ______imnida)

Introducing Your Age in Korean

Age makes a big difference in your interactions in Korea . You want to use more formal and polite language in introductions for people in a higher social position (your boss, your elders, etc.) and can use standard Korean with those who are the same age or younger than you.

“I am… years old” =

저는 ______살입니다 (jeoneun ______sarimnida)

저는 ______살이에요 (jeoneun ______sarieyo)

Please note that as you introduce your age, you should use the native Korean Number System. We have a complete guide for Korean numbers here . But here’s a quick refresher if you need it.

Introducing Where You Are From in Korean

Something that will often come up when you introduce yourself, your speaking partner will probably want to know where you’re from.

“I am from…” =

저는 ______에서 왔습니다 (jeoneun ______eseo wasseumnida)

저는 ______에서 왔어요 (jeoneun ______eseo wasseoyo)

Example Countries

You can fill in the blank with the country that applies to you. For more countries you can check our post about how to say “country” in Korean .

United States = 미국 (miguk)

Canada = 캐나다 (khaenada)

United Kingdom = 영국 (yeongguk)

Australia = 호주 (hoju)

The Philippines = 필리핀 (philliphin)

Singapore = 싱가폴 (singgaphol)

Introducing Your Occupation in Korean

Work is important in Korean culture and it’s likely to come up when you introduce yourself. Either during your self-introduction or as a question after you finish.

“I am a ______” =

저는 ______입니다 (jeoneun ______ imnida)

저는 ______이에요/예요 (jeoneun ______ iyeyo/yeyo)

You can use this sentence to introduce your name as well as to explain your occupation. Just replace the X with what you want to say.

Example Occupations

Student = 학생 (haksaeng)

Teacher = 선생님 (seonsaengnim)

Engineer = 엔지니어 (enjinieo)

Part-timer = 아르바이트생 (areubaiteusaeng)

Web-developer = 웹 개발자 (wep gaebalja)

Nurse = 간호사 (kanhosa)

Hairdresser = 미용사 (miyongsa)

Salesperson = 영업 사원 (yeongeob sawon)

Clerk = 사무원 (samuwon)

Conversation Topics

After the very basic questions Koreans will ask, you might want to introduce some other topics to keep the conversation going. Here are some basic sentences you can use.

“I learned Korean in…” =

저는 ______에서 한국어를 배웠습니다 (jeoneun ______eseo hangukeoreul baeweosseumnida)

저는 ______에서 한국어를 배웠어요 (jeoneun ______eseo hangukeoreul baeweosseoyo)

“I live in…” =

저는 ______에서 살고 있습니다 (jeoneun ______eseo salgo isseumnida)

저는 ______에서 살고 있어요 (jeoneun ______eseo salgo isseoyo)

“My hobby is…” =

제 취미는 ______이에요/예요 (je chwimineun ______ieyo/yeyo)

“…is one of my hobbies” =

______ 은/는 제 취미 중 하나예요 (______eun/neun je chwimi jung hanayeyo)

List of Hobbies

Reading = 독서 (dokseo)

Cooking = 요리 (yori)

Hiking = 등산 (deungsan)

Soccer = 축구 (chuggu)

Basketball = 농구 (nonggu)

Baseball = 야구 (yagu)

Piano = 피아노 (phiano)

Guitar = 기타 (githa)

Did you find today's lesson useful? How many opportunities have you already had to introduce yourself in Korean? What do you want to learn next? Let us know in the comments!

The post How to Introduce Yourself in Korean appeared first on 90 Day Korean® .

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How to Introduce Yourself in Korean: Step-by-Step Guide

Table of contents, why learn how to introduce yourself in korean.

Knowing how to introduce yourself in Korean is crucial for learning the language and understanding Korean culture. If you’re interested in Korean language and culture or planning a trip to Korea, learning how to introduce yourself in Korean is the first step. In this guide, I’ll provide a step-by-step process for how to introduce yourself in Korean, including essential phrases and basic grammar.

“Learning Korean based on theory or rules alone may not be enough to achieve your goals. To truly master the language, extensive practice is necessary. Are you looking for a Korean course that prioritizes practice? Join our free course and experience it for yourself now😊.”

Basic Korean Grammar for Introducing Yourself

To learn how to introduce yourself in Korean, it’s crucial to understand Basic Korean Grammar for Introducing Yourself. In Korean language, the word order follows subject-object-verb (SOV), which differs from English. Here are some essential sentence structures to keep in mind:

Example: 제 이름은 [Name]입니다. My name is [Name].

제 이름은 제인입니다. 제 (je) 이름은 (i-reu-meun) 제인입니다 (je-in-im-ni-da). 제 (my; in a humble way) 이름 (name) 은 (my topic is my name) 제인 (Jane) 입니다 (is; I’m telling you formally). My name is Jane.

Example: 저는 [Nationality] 사람입니다. I am from [Nationality].

저는 멕시코 사람입니다. 저는 (jeo-neun) 멕시코 (mek-si-ko) 사람입니다 (sa-ram-im-ni-da). 저 (I; in a humble way) 는 (I’m going to talk about me) 멕시코 (Mexico) 사람 (person) 입니다 (am; I’m telling you formally). I am from Mexico. Lit. I am Mexico person.

Step-by-Step Guide on How to Introduce Yourself in Korean

Now that you have a basic understanding of Korean grammar, let’s dive into the step-by-step process for how to introduce yourself in Korean.

The first step is to greet the person you’re introducing yourself to. The most common greeting in Korean is 안녕하세요 (annyeonghaseyo), which means “ hello .” Other common greetings include 안녕 (annyeong), which is less formal, and 반갑습니다 (bangapseubnida), which means “nice to meet you” and is commonly used in formal situations. It’s important to note that the level of formality used in the greeting should match the level of familiarity and social status of the person you’re speaking with.

안녕하세요, 반갑습니다. 안녕하세요 (an-nyeong-ha-se-yo), 반갑습니다 (ban-gap-seum-ni-da). 안녕하세요 (hello; in a polite way), 반갑 (to be glad) 습니다 (am; I’m telling you formally). Hello, nice to meet you. Lit. Hello, (I)’m glad.

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After exchanging greetings, the next step is to learn how to Introduce yourself in Korean by stating your name. In Korean, you can say “제 이름은 [Name]입니다” (je ireumeun [Name]imnida), which translates to “My name is [Name].” It’s essential to use appropriate honorifics while introducing yourself to someone who is older or of higher status.

안녕하세요, 제 이름은 존입니다. 만나서 반갑습니다. 안녕하세요 (an-nyeong-ha-se-yo), 제 (je) 이름은 (i-reu-meun) 존입니다 (jon-im-ni-da). 만나서 (man-na-seo) 반갑습니다 (ban-gap-seum-ni-da). 안녕하세요 (hello; in a polite way), 제 (my; in a humble way) 이름 (name) 은 (my topic is my name) 존 (John) 입니다 (is; I’m telling you formally). 만나 (to meet) 아서 (because) 반갑 (to be glad) 습니다 (am; I’m telling you formally). * 만나 + 아서 = 만나서 Hello, my name is John. Nice to meet you. Lit. Hello, my name is John. (I)’m glad because (I) meet (you).

Nationality

After introducing yourself by name, the next step in how to introduce yourself in Korean is to mention your nationality by saying “저는 [Nationality] 사람입니다” (jeoneun [Nationality]saramimnida), which translates to “I am from [Nationality].” This can help the other person understand your background and origin.

안녕하세요, 제 이름은 John이고 저는 미국 사람입니다. 안녕하세요 (an-nyeong-ha-se-yo), 제 (je) 이름은 (i-reu-meun) 존이고 (jo-ni-go) 저는 (jeo-neun) 미국 (mi-guk) 사람입니다 (sa-ram-im-ni-da). 안녕하세요 (hi; in a polite way), 제 (my; in a humbe way) 이름 (name) 은 (my topic is my name) 존 (John) 이고 (and) 저 (I; in a humble way) 는 (my topic is myself) 미국 (the United States) 사람 (person) 입니다 (am; I’m telling you formally). Hi, my name is John and I’m from the United States. Lit. Hi, my name is John and I’m United States person.

Next, you can mention your occupation by saying “제 직업은 [Occupation]입니다” (je jigeobeun [Occupation]imnida), which means “My occupation is [Occupation].” This can help the other person understand what you do for a living and can provide a conversation starter.

제 직업은 선생님입니다. 제 (je) 직업은 (ji-geo-beun) 선생님입니다 (seon-saeng-nim-im-ni-da). 제 (my; in a humble way) 직업 (occupation) 은 (my topic is my occupaton) 선생님 (teacher) 입니다 (is; I’m telling you formally). My occupation is a teacher. Lit. My occupation is teacher.

저는 엔지니어로 일합니다. 저는 (jeo-neun) 엔지니어로 (en-ji-ni-eo-ro) 일합니다 (il-ham-ni-da). 저 (I; in a humble way) 는 (my topic is myself) 엔지니어 (engineer) 로 (as) 일하 (to work) ㅂ니다 (I’m telling you formally). I work as an engineer. Lit. I work as engineer.

Hobbies and Interests

Lastly, you can mention your hobbies and interests by saying “제 취미는 [Hobby]입니다” (je chuimineun [Hobby]imnida), which means “My hobby is [Hobby].” This can help the other person get to know you better and can provide topics for further conversation.

제 취미는 독서입니다. 제 (je) 취미는 (chwi-mi-neun) 독서입니다 (dok-sseo-im-ni-da). 제 (my; in a humbe way) 취미 (hobby) 는 (my topic is my hobby) 독서 (reading) 입니다 (is; I’m telling you formally). My hobby is reading.

제 취미는 등산입니다. 제 (je) 취미는 (chwi-mi-neun) 등산입니다 (deung-san-im-ni-da). 제 (my; in a humble way) 취미 (hobby) 는 (my topic is my hobby) 등산 (hiking) 입니다 (is; I’m telling you formally). My hobby is hiking.

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Common Mistakes to Avoid When Introducing Yourself in Korean

When learning how to Introduce yourself in Korean, it can be nerve-wracking, particularly if you are not a native speaker. However, with adequate preparation and practice, you can confidently introduce yourself and avoid common mistakes. In this article, we will discuss some common mistakes to avoid when introducing yourself in Korean and provide tips on how to introduce yourself in Korean effectively.

Using Formal Language

One common mistake to avoid when learning Korean introducing Yourself in Korean is using informal language with someone who deserves formal language. In Korean culture, formal language is a way to show respect to those who are older or of higher status. For instance, when introducing yourself to a professor or a boss, it is crucial to use formal language to show respect. Failing to do so may result in the other person feeling disrespected or offended.

Forgetting to Use Honorifics

When learning How to Introduce Yourself in Korea, another mistake to avoid is forgetting to use honorifics when addressing someone older or of higher status. Honorifics are a way to show respect to those who are of a higher age or status. Failing to use honorifics can come across as rude or disrespectful. For instance, instead of saying “What is your name?” to an older person, it is better to use the honorific form “What is your respected name?” or “What is your honorable name?” to show respect.

Mispronouncing Words or Using Incorrect Grammar

Mispronouncing words or using incorrect grammar can also be a common mistake when introducing yourself in Korean. Korean pronunciation and grammar can be challenging for non-native speakers. However, making an effort to learn proper pronunciation and grammar can go a long way in making a good impression. You can practice by listening to Korean speakers or taking a Korean language class.

Being Too Verbose or Using Complicated Sentences

When introducing yourself in Korean, it’s important to keep your sentences clear and concise. Being too verbose or using complicated sentences can make it difficult for the other person to understand you. Keep your introduction simple and to the point. For example, instead of saying “I am a student who is currently studying Korean language and culture at a university in Seoul,” you could say “I am a student studying Korean in Seoul.”

Learning How to Introduce Yourself in Korean can be a fantastic way to connect with Korean speakers and understand their culture. Remember to use proper honorifics and show respect when addressing someone. Regularly practicing your Korean language skills can also help you improve. With these tips and a bit of practice, you will be able to confidently and fluently introduce yourself in Korean.

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• Feb 6, 2020

Introducing Yourself In Korean Is Easier Than You Think!

Updated: Feb 10, 2020

Introducing yourself in Korean isn´t as daunting and difficult as you may think. Once you got the basic rules down its actually pretty easy! Many of my students are pleasantly surprised after our first class together at how easy it actually is to put together a very basic but important sentence in Korean. I always tell my students when it comes to learning Korean try to make your sentences as simple as possible and you will have no problem!

So let's get straight to it! How do you introduce yourself in Korean?

The first thing you should know is that Korean sentence patterns, unlike English, are SOV (Subject + Object + Verb). Yeah, I know what you are thinking! So that means Koreans speak ¨backwards¨? Well if you compare it to English or many other Western languages; yes. In Korean, the verb goes at the end of the sentence .

Note: Koreans typically introduce themselves by saying ¨ I am_____¨

For example:

I am Nathan.

I Nathan am. ( 저는 네이뜬이에요 . )

Break down:

는 (neun): Topic marker used to describe and state facts after nouns (* These don´t exist in English)

이에요 (iaeyo): am

The Verb ¨To Be¨ In Korean

The verb ¨ to be ¨ in Korean is 이다 ( i da).

Great news! Korean does not conjugate verbs to agree with the subject. For example, I am, He is, She is, They are, etc. Nope! These don´t exist in Korean!

Verb conjugations depend upon the verb tense . Korean conjugations in Korean grammar decide the tense, tone, and mood of the speaker. Don´t worry, it's not as complicated as it sounds!

이다 in formal present tense has 3 forms which all can mean am, is, are, & it is depending on the context of the sentence.

입니다 (ibnida) usually used in official documents, news or when meeting someone for the first time. This is the highest formal version of am, is, & are.

이에요 ( iaeyo ) & 예요 ( yaeyo ) are used in everyday Korean. 이에요 ( iaeyo ) is used if the noun you are using in your sentence has a bottom consonant. 예요 ( yaeyo ) is used when there is no bottom consonant.

네이뜬이에요! ( It´s Nathan )

Nathan written in Hangul (Korean alphabet) has a bottom consonant (ㄴ) so we have to use 이에요 ( iaeyo ).

사라예요 ( It´s Sarah )

Sarah written in Hangul does not have a bottom consonant so we have to use 예요 ( yaeyo ).

Topic Markers ( 은, 는)

Topic marking particles are used to let the other people know what you are talking about, describe things, and state facts. Topic marking particles are attached after nouns.

은 (eun) is used if your noun has a bottom consonant.

는 (neun) is used if your noun does not have a bottom consonant.

Today since we are learning how to introduce ourself and talk about ourself we have to attach a topic marker to the subject of the sentence which is 저 (I)

Put It All Together

저는______예요/이에요. I am________

Jo neun_______( yaeyo )/ ( iaeyo )

저 는 사라 예요 . ( Jo neun Sarah yaeyo. )

Direct English translation: I Sarah am.

I am Hakjin.

저 는 학진 이에요 . ( Jo neun Hakjin iaeyo )

I am a student.

저는 학생 이에요. (Jo neun haksaeng iaeyo )

I am a teacher.

저는 선생님 이에요. ( Jo neun seon saeng nim iaeyo )

So now that we know how to describe ourself, how would you describe someone else using a topic marker and 이에요 ( iaeyo )/ 예요 ( yaeyo )?

사라 는 학생이에요 .

Sarah is a student .

아빠 는 미국 사람이에요 .

Dad is American .

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How to Introduce Yourself in Korean

Last Updated: December 13, 2023 Fact Checked

This article was co-authored by wikiHow staff writer, Jennifer Mueller, JD . Jennifer Mueller is a wikiHow Content Creator. She specializes in reviewing, fact-checking, and evaluating wikiHow's content to ensure thoroughness and accuracy. Jennifer holds a JD from Indiana University Maurer School of Law in 2006. There are 9 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 274,608 times. Learn more...

Whether you're traveling in South Korea or want to socialize with a local Korean immigrant community, you need to introduce yourself. First, say “hello” with a polite 안녕하세요 (“annyeong haseyo”) while bowing slightly at the waist. Shake hands if one is offered to you, and then you can state your name and continue the conversation. Introductions may seem intimidating, but as long as you speak politely and show respect, you'll be making new Korean friends in no time. [1] X Research source

Meeting New People

• If you're greeting a child for the first time, it's fine to simply say "안녕" (annyeong), which is the informal way to say "hello."

Tip: In Korean, there are formal, polite, and informal ways of speaking. The character 요 (yo) is an indication that you're using the polite way of speaking, which is acceptable anytime you're speaking to strangers, or to people older than you or in a position of authority.

• Close your eyes or keep them downcast as you bow. However, be alert for an extended hand. A handshake may accompany bows, particularly among men greeting other men.

• You can also use "식사하셨어요?" (shiksa hashutsuyo?) This question literally means "Have you eaten?" However, it's a common way to ask "How are you?" in Korean culture. The typical response to this question is "네 했어요" (ae haeseoyo), which means "Yes, I've eaten." Keep in mind that despite the translation, if you're asked this question by someone, they aren't asking you to out to eat with them.

• For example, if your first name is Karen, you might say "je ireumeun Karen imnida."
• It's true that there are often "translations" for names in different languages, particularly European languages. However, this is not the case for Korean. Simply use your regular name.
• After introducing yourself, you might ask "이름이 무엇입니까?" (ireumi mueosimnikka?) This question means "what's your name?" When the person responds, you might say "반갑습니다" (bangapseumnida), which means "pleased to meet you." [6] X Research source

Respecting Korean Culture

• If you're greeting someone older than you, of high social status, or in a position of authority, you may bow as much as 45 degrees forward, keeping your head lowered.

Tip: It would never be considered a mistake to bow too deeply. However, if you don't bow deeply enough, you might offend. For that reason, it's best to err on the side of a deeper bow.

• Using both hands is a sign of respect. You can also support your right wrist with your left hand as you shake the person's hand.

• This can be difficult if you come from a Western culture that values eye contact. If you find yourself struggling, remain conscious of your eyes and look away quickly if you happen to make eye contact. Brief eye contact is no big deal, but sustained eye contact may be problematic.

• There are many titles in Korea that are the equivalent of "father," "teacher," or "doctor." The title you use may depend on your relationship to the person. For example, if you introduce yourself to the parents of your Korean friend, they may want you to use the "father" and "mother" titles for them.
• If you don't know the right title to use with a person, add "씨" (ssi) to the end of their full name. This is the equivalent of using "Mr." or "Ms." in English. For example, if you are introduced to Mr. Kim Sung-Yoon and you didn't know any other title to use, you could address him as "Kimsungyoonssi." [11] X Research source

Community Q&A

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• ↑ https://www.omniglot.com/language/phrases/korean.php
• ↑ https://theculturetrip.com/asia/south-korea/articles/korean-greetings-you-should-know/
• ↑ https://blogs.transparent.com/korean/hello-and-hi-in-korean/
• ↑ https://app2brain.com/learn-languages/korean/conversations/
• ↑ https://blogs.transparent.com/korean/as-a-tourist-in-korea-greeting-simple-conversation/
• ↑ https://theculturetrip.com/asia/south-korea/articles/15-korean-phrases-you-need-to-know/
• ↑ https://culturalatlas.sbs.com.au/south-korean-culture/south-korean-culture-greetings
• ↑ https://culturalatlas.sbs.com.au/south-korean-culture/south-korean-culture-communication
• ↑ https://blog.lingodeer.com/korean-honorifics/

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TOPIK Essay Writing Guide (쓰기 가이드)- Beginner Level

And here is how the TOPIK examiners evaluated it:

Second model essay

Third model essay: 

Dr. Satish Satyarthi

Satish is the Founder and CEO of TOPIK GUIDE. He is passionate about languages. He created TOPIK GUIDE website to help Korean language learners learn Korean effectively, succeed in TOPIK test and achieve their goals. He has a PhD in Korean language. His research area has been 'Teaching Korean as a Foreign Language (외국어로서의 한국어교육)'. He is a Seoul National University (GKS) alumni. He has been active in Korean language teaching and research for more than 10 years. We are a team of passionate researchers from Seoul National University specializing in Korean language and linguistics. We are committed to helping international students prepare for the TOPIK test. You can connect with us on Facebook , Twitter , Google+ or YouTube

THANK YOU SO MUCH ı DOWNLOADED THE PAPERS gOOD WORKS

@Serife OK.. so you were finally able to download the papers.. that’s good… All the best..

Thanks for ur great work TOPIK GUIDE.I also request to upload about essay writting guide For Inter-Mediate level too.

It’s very interesting to see examples of how the essays are evaluated, thank you very much for this post. I’d also be glad if you could do something similar for the Intermediate level as well.

Appreciate ur efforts thanks for the papers….

Very useful information….

Hey useful information..

Hey can you plz let us know the eligibility for appearing for TOPIK

Thanks for the details…can you plz tell how much weightage is given for essay writing?

It’s 30% of the writing section…

thanks…

thank you for all the information its helps a lot….GOD bless you and your family!

yes tax alot u,,,these are real instructions

Where did you find the evaluation rubric and sample evaluation scores? I’ve looked for days over much of the Internet to try to find your source, but I can’t find anything. Were these posted to topik.go.kr long ago?

Yes, it was released long ago. I don’t have the exact link but I think it was somewhere in their 공지사항 in Kroean.

Hi are there any downloadable files of these lessons available? thanks

Hello sir I wanna give topik exam this year 2020 i wanna know all details and when to register for topik exam and can i give any level exam

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TOPIK Beginner Writing Samples (Tests 10-20)

One of the BEST things I ever studied while preparing for the TOPIK test was the sample answers that the TOPIK website provides.

TOPIK Beginner Writing Topics & Samples (10-20).DOCX

So, I’ve decided to collect ALL the previous TOPIK Writing topics and examples provided by www.topik.go.kr into a single document. However, since there are so many, I’ve decided to split the larger document into 2 smaller parts with 10 previous tests each (TOPIK Writing began from test 10).

Here are Tests 10-20, while this link will take you to Tests 21-30 :

Why were these writing samples so great?

Simply, I could see some of the grammar structures and vocabulary that would give me more points on the test if I used them. If you want some specific examples of how I studied with these sample essays, check out my post on tests 21-30 where I detail some of the grammar structures I learned and used:

• TOPIK Beginner Writing Samples (Tests 21-30)

And if you just want to see the entire collection of Beginner Writing Topics, click below:

• TOPIK Beginner Writing Topics & Tips (한국어능력시험 초급 쓰기)

How have the TOPIK sample answers helped you on TOPIK?

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A note about topik registration in korea.

Warning: TOPIK II test locations fill up quickly! Less than 2 hours after registration opened, I was 17,878 in line. After 5 hours, everything that opened for registration today was full. Be early, or be sorry. (Or wait for IBT TOPIK from 2023.)

120 Days to TOPIK #1 – Gather Resources

Gather your resources! This is one of my favorite parts of any new Challenge because it can be fun to look over the kinds of materials you WANT to use and the kinds of things you WANT to learn (plus, Continue reading 120 Days to TOPIK #1 – Gather Resources

How I Will Study for the TOPIK II in 120 Days (and You Can Join Me)

Well, that was unexpected. I recently wrote a post outlining my plan to cram for the TOPIK II in 30 days. I had been planning to take the test on July 19, 2015. But, I guess plans change. As it turns Continue reading How I Will Study for the TOPIK II in 120 Days (and You Can Join Me)

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Tips for improving Korean essay writing

After more than a year of attending advanced Korean classes and regularly writing and reviewing 500-800 character essays with my teacher, I’ve accumulated a few useful tips for improving long-form writing that I thought I’d share here.

I’ll preface this by saying few people write well in any language, even among native speakers. I’m a writer and storyteller in both my professional and personal life and I know just how hard it is to build compelling rhetoric using effective, engaging language on any topic. So, following these “quick tips” won’t make you a good writer in Korean — that will take years of practice reading and writing, just as it would in English. But it may help you get started on the road to sounding more natural.

Caveat : This is only one language learner’s experience (mine) and one language instructor (my teacher)’s advice, so take with a grain of salt.

Master written language

This means practicing and getting comfortable with plain speech. Plain-style Korean, or 해라체, is used in books, newspapers, blogs — basically any form of writing where you’re not directly addressing someone else. For example, you’d use plain style to write a blog post in Korean, but use proper politeness (i.e. -요 or -ㅂ니다) levels when responding to comments. Sentences in plain-style Korean end with -(ㄴ)다 (present/future) or -ㅆ다 (past).

There are a number of useful textbooks out there that cover Korean grammar; I’m personally a fan of 빈도별 토픽 for more advanced learners and the Integrated Korean series for beginners and intermediate learners. When going through these books, keep in mind that not all verb endings are appropriate for written Korean. Endings like -지요 and -잖아요, for example, only make sense when you’re talking to someone else.

Use inductive reasoning

Obviously not a hard and fast rule (depends on the discipline, writer’s style, etc.), but after reading a number of Korean op-eds and personal essays, the biggest difference between Korean and English essay writing that I’ve noticed is the underlying logic of the work.

Korean essays are mostly built on inductive reasoning: they start out with anecdotes, examples, and research to draw the reader step-by-step to the main point of the essay. If you’re reading a long opinion piece in Korean, you might make it through 50% or more before you realize what exactly the author’s trying to say.

This is counter to the typical “five paragraph essay” taught in American high schools, where you’re told to state a main point for each paragraph and then support it with evidence (more deductive than inductive reasoning).

Tip! I’ve found that because of this logic structure, skimming long chunks of Korean prose — especially during TOPIK — is challenging for me.  If you’re pressed for time, read the last couple sentences of every paragraph to get a decent tl;dr.

Memorize transition words and phrases

These are words like 그런데, 그리고, 게다가, 반면에, etc., that link one sentence to the next or one paragraph to the next. When you’re reading essays, news articles, or even TOPIK passages, take a second to identify and write down these transition words/phrases. Memorize them and practice using them in your own writing, so you won’t default to the boring ones I listed as examples above.

Put the most important part of the sentence first

One of my favorite things about Korean writing is the flexibility of word order within a sentence. That said, I often get told by my teacher to not be so careless about it when writing essays. A writing tic of mine, for instance, is to include every relevant detail that I possibly can into a relative clause that modifies the topic or subject of my sentence; that means my sentences are “top heavy” with the most important part usually coming near the end. In longer compositions, though, it’s important to make sure your key point shows up at the  beginning of the sentence for clarity.

For example, take a look at these two sentences. The first is what I wrote and the second is a revision.

(1) 마지막으로 직장 관련 이야기가 듣기 싫다는 대답을 선택한 여성들에 비해 2배 이상 많은 남성들이 있다는 결과가 나왔다.

(2) 마지막으로, 여성들에 비해 2배 이상 많은 남성들이 직장 관련 이야기가 듣기 싫다는 대답을 선택한 결과가 나왔다.

I was trying to describe that, according to the results of the given survey, more than twice the number of men versus women said they did not want to discuss work [with their extended families during the holiday]. In sentence (1), the key point ‘여성들에 비해 2배 이상 많은 남성’ shows up at the end of the sentence; in sentence (2), it shows up right after the transition word.

Use! The! Right! Particles!!

Believe me when I say that it is worth investing time into understanding the difference between topic particles (은/는) and subject particles (이/가). For a lot of beginners, this is one of the most difficult concepts to grasp, especially if English is your native language. If you’re only writing a couple short sentences at a time, to a limited (!) extent, you can get by mixing up the two without dire consequences.

For example:

(1) 그 남자 는 나를 좋아한다고 고백했다. (2) 그 남자 가 나를 좋아한다고 고백했다.

Both sentences have differences in nuance but more or less mean the same thing.

When sentences get long with different topics, subjects, and clauses, using the wrong particle can really mess up the meaning of your sentence. And when you’re introducing different points in an essay, mastering 은/는 and 이/가 will help direct the logical flow of your writing and lend it clarity. The best way to grasp particles? Write a lot, but don’t just write disparate sentences. Write a paragraph or two on one idea and then have your writing reviewed by a native speaker.

Synonyms are your friend

This is good writing advice no matter what language you’re writing in. No one wants to read the same adjective or verb over and over again, so it’s good to pay attention to different ways to say the same thing. This is particularly important, I think, for the analytical writing part of TOPIK, which asks you to describe the results of a survey or research study. You’ll be using a lot of phrases like “X increased by Y%” or “A decreased by B%” or things like “it was revealed that XX,” “the results showed ,” “the participants chose , ”  etc.

Tip! Compared to the longer essay in TOPIK II, the analytical essays are actually where you can improve your score the fastest, in my opinion. One easy way to do that is by diversifying your sentences with different synonyms and phrases related to analysis and trends. On one of my early practice tests, I used the word 증가하다 three sentences in a row — don’t do that.

Last but not least: read actively

An obvious one, but worth mentioning. Now, when I read non-fiction in Korean, I don’t just read for comprehension. I read for writing style. And I mean, I really break down the structure of the composition. I start out by picking out where the main idea shows up in each paragraph and then jot it down. Then I try to pay attention to how the sentences are connected to one another, noting specifically how the author shifts topics and subjects from sentence to sentence and how transition phrases/words help build the logic of the narrative. And then I try putting in any new words and sentence structures/phrases I’ve learned into practice.

In order to build your vocabulary, it’s important to read widely. But in order to become a better writer, I think it’s important to read closely .

Closing thoughts

I think being able to write naturally, using standard grammar, is an important tenet of fluency. But I don’t think you have to be a “good” writer to consider yourself fluent — though, of course, it depends on what your ambitions and/or goals are.

In English, writing is like breathing for me. So it’s important to me that I can write something that would be considered generally good writing in Korean, not just comprehensible or “good for a foreigner.” That’s what I’m working toward, at least.

To that end, I do have a ((new)) blog in Korean, up on the interwebs somewhere. I’m waiting until I write a few more posts before I officially share it here, but if keep your eyes peeled if you’re interested!

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10 responses to “tips for improving korean essay writing”.

what do you think about the hanguel-only thing as it relates to writing?? do you find it limits your writing ie changing a word because there’s a lot of homophones?

That’s an interesting perspective. I don’t think I’ve ever felt limited by Hangeul as a writer. If I’m worried about being misunderstood because of a homophone issue, I just look up the hanja and put it in parentheses. As a reader, though, I can see how Hangeul might seem limiting, e.g., looking up hanja is an extra step I have to go through to understand whatever I’m reading, or worse, if there is no hanja, I expend more mental energy trying to figure what the author means.

Like Liked by 1 person

also have you noticed bad writing in Korean in korean newspapers, articles etc now that you’re more skilled in writing?? Or are there certain things you see korean writers do that annoy you ??

I was reading the japanese internet about reading korean and of course there’s negative stuff. I would love to hear your thoughts about it. if you use rikai-chan or some pop-up dictionary i’m sure you can understand it… i’ll paste it…

漢字を無くしたから意味を文字で伝えるのが難しい なので平易な言葉で伝えないと読み手に書き手の意思を伝えられない 冗長になり論点がぼけるのはハングルの特徴だから仕方ない

書くための文字→ハングル 読むための文字→漢字

これが言語や単語にまで影響及ぼしてるだけ

++ 南朝鮮の新聞記事は長文駄文意味不明が多いよね 文章「表現力」以前に、思考回路に何かしらの障害がありそう 目の前の事象を「しあわせ回路」や「反日回路」に入力し、「願望」の粉かけて、ビビンパのようにまじぇまじぇしないとあんな文章は出力できないよ

なんかの調査で韓国人は平均ＩＱが高いと言われてホルホルしてたけど （１位香港　2位韓国　3位日本・・・・） ＩＱテストのためのテスト勉強させてる国がどこかにあるとかｗ ＩＱ高くてもあんだけ非論理的だし 自前でロケットさえ飛ばせない ノーベル賞なんか皆無だし(金で買える平和賞は除く） ああ、むしろＩＱなんて全然意味ねえじゃんて気づかせられた件でした

++ 韓国の新聞の社説を読んでると、論旨が不明瞭で様々な故事を出した後に何の論理的な展開も無く○○が悪いという結論で終ってる事が多くて何を伝えたい文章なのかさっぱり分からない事が多い。

普通の新聞記事でも、感情的な言い回しが多くて、記者はニュースを伝えたいのか、怒り嘆きを伝えたいのかよく分からない文章になってる事が多い。

そんな文章を日常的に読んでいたら、文章を読むのが苦手になっても不思議ではないし、論理思考が出来なくなっていくのも無理は無い。

++ だからなのか、韓国の新聞て論説委員からして文章力がない。ただダラダラ長いだけで何が言いたいのかさっぱり伝わらない。記事タイトルと書いてる内容がズレてることも多々ある。こんなレベルの低い文章ばかりなのに、読解力を求めらても何の意味もないから、そのままで良いと思う。

+ 韓国の新聞のコラムでも 読みたくなるような、読ませる文章ではないよね。ドヤ顔でどこかの国の引用から始まるのが ほとんど。 ++

童貞　同情　　　同志　冬至　　　史記　詐欺 紳士　神社　　　郵政　友情　　　首相　受賞 火傷　画像　　　市長　市場　　　風速　風俗 映画　栄華　　　戦死　戦士　　　歩道　報道 犬喰　見識　　　日傘　量産　　　数値　羞恥 お腹　お船　　　烈火　劣化　　　主義　注意 読者　独自　　　団扇　負債　　　停電　停戦 大使　台詞　　　諸国　帝国　　　諸島　制度 声明　姓名　　　無力　武力　　　全員　田園 定木　定規　　　全力　電力　　　代弁　大便 捕鯨　包茎　　　地図　指導　　　素数　小数 対局　大国　　　誇張　課長　　　インド　引導 初代　招待　　　朝鮮　造船　　　駅舎　歴史 発光　発狂　　　定額　精液　　　火傷　画像 反戦　反転　　　反日　半日　　　武士　無事 大便　代弁　　　無力　武力　　　電車　戦車 連覇　連敗　　　恨国　韓国　　　祈願　起源 競技　景気　　　放火　防火

ハングルでは全部同じ。 同音異義語というのみならず表記も全く同じ。 前後の文脈で区別するしかない。

例： 釣船の操船で有名な朝鮮人たちの祖先が造船した商船に率先して乗船し商戦に挑戦 ↓ チョスンのチョスンで有名なチョスン人たちのチョスンがチョスンしたチョスンにチョスンしてチョスンしチョスンにチョスン

http://u1sokuhou.ldblog.jp/archives/50389367.html

kaikaiの他所でも書いたが、 文章のプロである朝鮮日報・中央日報・等々の文章が下手で驚く。 コラムを書く役職の有る記者ですら、無駄な前振り、論点が行方不明など酷い。

さらに酷いのは新聞記者ではない何かの教授や研究員・学者の類が描く時だ、 目が回るほどに下手だ。 日本のそこいらの素人のblogの方が上手い。

他の国ではそういうことは無い、朝鮮だけの大きな特徴でとにかく文章が下手糞だ。 翻訳の問題ではない。 文章の構成能力や論理的な組み立てが出来てない性質のものだ。

一言で言うと「馬鹿」としか思えない。　いや、馬鹿なんだろう。

62名無し1年前ID:QxMDg2NTE(1/2) >>59 >>58 >>60 向こうの記事読んでそれ感じる。漢字捨てたせいか専門的な単語を使うのにためらうというか使えないというか。そのために意味を分かりやすくしようと変な例えを出してやたら文章が長くなってしまう。 その変な例えの殆どが詩的なんだが自分からすると状況が一致しないか書いた記者自身の文章酔ってるかのように感じてしまい気持ち悪い。

起承転結の起に入るまでが長かったり承が訳が分からなかったり転に至っては絶対間違ってない自信なのか存在しない、それとなぜかなんの脈絡もなく日本が出てきたりと。 ここに来る韓国人の文章は機械翻訳ではあるが変な比喩が無く言いたい事が分かる読みやすい文章なので全然記者よりも文章が優れてるように見える。

i FORGOT THIS この人の発言は、韓国人の文章（新聞記事等）に通じるものがありますね。言い訳と希望的観測を交えて、整合性のない事を平然と悪びれる事なく（息を吐くように）言う。

2018/04/02 [05:52:03] jk 一方、韓国の新聞記事は、的を得ているとは思えない故事や格言、故人の言を引いて、中身の薄い内容を難しく伝える。

Whoops your last two comments got flagged as spam so I’m seeing them just now. Hmm, I wouldn’t say that I’m skilled enough to differentiate between good and bad journalistic writing just yet. I /can/ tell the skill difference between, say, a newbie writer who publishes web novels on Naver and more experienced novelist.

In terms of annoying things Korean writers do… hm… I don’t think I’m widely read enough to make too many sweeping judgments here but I have noticed that in a lot of literary writing, Korean authors tend to use adjectives that are near synonyms of each other, one after the other in the same sentence. e.g. “her face was pale and white” or “the rain fell heavily and strongly.” Sometimes I think the slight nuance helps add atmosphere but when it happens sentence after sentence it becomes tedious to read (and translate). The other thing I personally like in my fiction is varying sentence lengths. It changes up the . cadence of the prose and keeps things interesting. I find that a lot of Korean sentences are similar in length (longggg) one after the other.

Interesting… I’ll take a look at those Japanese comments (let’s see how far I get before the Kanji kicks my ass and I need to use a dictionary lol). Thanks for sharing!

WelL theres lots of cool popup dictionaries to save you time like rikaichan and yomikun.

From my limited experience of reading Korean articles I have to agree with the Japanese people lol. They were just describing what I was thinking but wasn’t confident enough to say since reading Korean isn’t that comfortable at times. Like you I don’t think I’m good enough to decide whether someone’s writing is bad or good

here’s one of the original sites in case you were curious https://kaikai.ch/board/2377/

韓国の新聞メディアや教授のコラムって、その論文全体を通して主張したい１つの軸が不明瞭で、一貫性がなく、要点がないんだよね。 だから「韓国人の書く文章は無駄に長い」「読むだけ無駄だった（重要な事が書いてない）」という感想持つ日本人は多い。

“起承転結”あるいは“起転結” これを韓国人は文章として成立させてない。 日本だと、大学で論文の書き方は骨身に叩き込まれるんだけど、韓国は何をやってるんだろ？

this is post 60 韓国の新聞のコラムとかって 内容の核心に入るまで長くて 余計な知識披露が多い、文字スペース 考えろよ。

they were the posts that post 62 responded too.

I love this! My long term goal is also to write as well and with as much confidence in Korean as I do in English, so this was extremely helpful. Can’t wait to check out your Korean-language blog — I started one a few months ago but only have three posts so far ㅎㅎ 화이팅!

I’m just a beginner but I think these are very valuable advice even for beginners to keep in mind as they progress.

The point about plain style especially hit home for me, because for the longest time I didn’t even know it existed. Every single textbook I have only ever mentioned -이에요/예요 and -(스)ㅂ니다. This made it very difficult to search for anything online as most blogs and websites are written in plain style, and I wasn’t able to read even the simplest stories written in Korean because I was not familiar with the endings.

I wish textbooks would at least mention the existence of this style sooner.

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I’m so glad found the post valuable, Luna! Thanks for reading. :)

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• School Education /

Essay on Myself: 100 Words, 250 Words and 300 Words

• Updated on  
• Mar 12, 2024

Every Individual is different from each other and it is important to self-analyze and know about yourself. Only you can know everything about yourself. But, when it comes to describing yourself in front of others many students fail to do so. This happens due to the confusion generated by a student’s mind regarding what things to include in their description. This confusion never arises when someone is told to give any opinion about others. This blog will help students and children resolve the confusion and it also includes an essay on myself. 

While writing an “essay on myself” you should have a unique style so that the reader would engage in your essay. It’s important to induce the urge to know about you in the reader then only you can perform well in your class. I would suggest you include your qualities, strengths, achievements, interests, and passion in your essay. Continue Reading for Essays on myself for children and students!

Quick Read: Speech on Earth Day

Table of Contents

• 1 Long and Short Essay on Myself for Students
• 2 Tips to Write Essay on Myself
• 3 100 Words Essay on Myself
• 4 250 Words Essay on Myself
• 5 10 Lines on Myself Essay for Children
• 6 300 Words Essay on Myself

Quick Read: English Essay Topics

Long and Short Essay on Myself for Students

Mentioned below are essays on myself with variable word limits. You can choose the essay that you want to present in your class. These essays are drafted in simple language so that school students can easily understand. In addition, the main point to remember while writing an essay on myself is to be honest. Your honesty will help you connect with the reader.

Tell me about yourself is also one of the most important questions asked in the interview process. Therefore, this blog is very helpful for people who want to learn about how to write an essay on myself.

Tips to Write Essay on Myself

Given below are some tips to write an essay on myself:

• Prepare a basic outline of what to include in the essay about yourself.
• Stick to the structure to maintain fluency.
• Be honest to build a connection with the reader.
• Use simple language.
• Try to include a crisp and clear conclusion.

100 Words Essay on Myself

I am a dedicated person with an urge to learn and grow. My name is Rakul, and I feel life is a journey that leads to self-discovery. I belong to a middle-class family, my father is a handloom businessman, and my mother is a primary school teacher .

I have learned punctuality and discipline are the two wheels that drive our life on a positive path. My mother is my role model. I am passionate about reading novels. When I was younger, my grandmother used to narrate stories about her life in the past and that has built my interest towards reading stories and novels related to history.

Overall I am an optimistic person who looks forward to life as a subject that teaches us values and ways to live for the upliftment of society.

Also Read: Speech on Discipline

250 Words Essay on Myself

My name is Ayushi Singh but my mother calls me “Ayu”. I turned 12 years old this August and I study in class 7th. I have an elder sister named Aishwarya. She is like a second mother to me. I have a group of friends at school and out of them Manvi is my best friend. She visits my house at weekends and we play outdoor games together. I believe in her and I can share anything with her.

Science and technology fascinate me so I took part in an interschool science competition in which my team of 4 girls worked on a 3-D model of the earth representing past, present, and future. It took us a week to finish off the project and we presented the model at Ghaziabad school. We were competing against 30 teams and we won the competition.

I was confident and determined about the fact that we could win because my passion helped me give my 100% input in the task. Though I have skills in certain subjects I don’t have to excel in everything, I struggle to perform well in mathematics . And to enhance my problem-solving skills I used to study maths 2 hours a day. 

I wanted to become a scientist, and being punctual and attentive are my characteristics as I never arrive late for school. Generally, I do my work on my own so that I inculcate the value of being an independent person. I always help other people when they are in difficult situations. 

Also Read: Essay on the Importance of the Internet

10 Lines on Myself Essay for Children

Here are 10 lines on myself essay for children. Feel free to add them to similar essay topics.

• My name is Ananya Rathor and I am 10 years old.
• I like painting and playing with my dog, Todo.
• Reading animal books is one of my favourite activities.
• I love drawing and colouring to express my imagination.
• I always find joy in spending time outdoors, feeling the breeze on my face.
• I love dancing to Indian classical music.
• I’m always ready for an adventure, whether it’s trying a new hobby or discovering interesting facts.
• Animals are my friends, and I enjoy spending time with pets or observing nature’s creatures.
• I am a very kind person and I respect everyone.
• All of my school teachers love me.

300 Words Essay on Myself

My name is Rakul. I believe that every individual has unique characteristics which distinguish them from others. To be unique you must have an extraordinary spark or skill. I live with my family and my family members taught me to live together, adjust, help others, and be humble. Apart from this, I am an energetic person who loves to play badminton.

I have recently joined Kathak classes because I have an inclination towards dance and music, especially folk dance and classical music. I believe that owing to the diversity of our country India, it offers us a lot of opportunities to learn and gain expertise in various sectors.

My great-grandfather was a classical singer and he also used to play several musical instruments. His achievements and stories have inspired me to learn more about Indian culture and make him proud. 

I am a punctual and studious person because I believe that education is the key to success. Academic excellence could make our careers shine bright. Recently I secured second position in my class and my teachers and family members were so proud of my achievement. 

I can manage my time because my mother taught me that time waits for no one. It is important to make correct use of time to succeed in life. If we value time, then only time will value us. My ambition in life is to become a successful gynaecologist and serve for human society.

Hence, these are the qualities that describe me the best. Though no one can present themselves in a few words still I tried to give a brief about myself through this essay. In my opinion, life is meant to be lived with utmost happiness and an aim to serve humanity. Thus, keep this in mind, I will always try to help others and be the best version of myself.

Also Read: Essay on Education System

A. Brainstorm Create a format Stick to the format Be vulnerable Be honest Figure out what things to include Incorporate your strengths, achievements, and future goals into the essay

A. In an essay, you can use words like determined, hardworking, punctual, sincere, and objective-oriented to describe yourself in words.

A. Use simple and easy language. Include things about your family, career, education, and future goals. Lastly, add a conclusion paragraph.

This was all about an essay on myself. The skill of writing an essay comes in handy when appearing for standardized language tests. Thinking of taking one soon? Leverage Live provides the best online test prep for the same. Register today and if you wish to study abroad then contact our experts at 1800572000 .

Kajal Thareja

Hi, I am Kajal, a pharmacy graduate, currently pursuing management and is an experienced content writer. I have 2-years of writing experience in Ed-tech (digital marketing) company. I am passionate towards writing blogs and am on the path of discovering true potential professionally in the field of content marketing. I am engaged in writing creative content for students which is simple yet creative and engaging and leaves an impact on the reader's mind.

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Transcript: Ezra Klein Interviews Dario Amodei

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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.”.

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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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