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College Essays

For the 2021-2022 admissions cycle, MIT admitted about 4% of applicants. If you want to be one of these lucky few, you'll need to write some killer MIT essays as part of your own Massachusetts Institute of Technology application.

In this article, we'll outline the MIT essay prompts and teach you how to write MIT supplemental essays that will help you stand out from the thousands of other applicants.

What Are the MIT Essays?

Like most major colleges and universities, MIT requires its applicants to submit essay examples as part of your application for admission.

MIT has its own application and doesn't accept the Common Application or the Coalition Application. The MIT essay prompts you'll answer aren't found on any other college's application.

There are four MIT supplemental essays, and you'll need to answer all four (approximately 200 words each) on various aspects of your life: a description of your background, what you do for fun, a way that you contribute to your community, and a challenge that you have faced in your life.

The MIT essay prompts are designed specifically to get to the heart of what makes you you . These essays help the admissions committee get a holistic picture of you as a person, beyond what they can learn from other parts of your application.

2022-2023 MIT Essay Prompts

The MIT supplemental essays are short, and each one addresses a different aspect of your identity and accomplishments.

You'll submit your essays along with an activities list and a self-reported coursework form as Part 2 of your MIT application. MIT structures its application this way because they rely on a uniform application to help them review thousands of applicants in the most straightforward and efficient way possible.

You need to respond to all five of the MIT essay prompts for your application.

Here are the 2022-2023 MIT essay prompts:

We know you lead a busy life, full of activities, many of which are required of you. Tell us about something you do simply for the pleasure of it.

Describe the world you come from (for example, your family, school, community, city, or town). How has that world shaped your dreams and aspirations?

MIT brings people with diverse backgrounds and experiences together to better the lives of others. Our students work to improve their communities in different ways, from tackling the world’s biggest challenges to being a good friend. Describe one way you have collaborated with people who are different from you to contribute to your community.

Tell us about a significant challenge you’ve faced (that you feel comfortable sharing) or something that didn’t go according to plan. How did you manage the situation?

Now that we know what the prompts are, let's learn how to answer them effectively.

MIT Essays, Analyzed

In this section, we'll be looking at each of the five MIT essays in depth.

Remember, every applicant must answer every one of the MIT essay prompts , so you don't get to choose which essay you would like to write. You have to answer all five of the MIT essay prompts (and do so strongly) in order to present the best application possible.

Let's take a look at the five MIT supplemental essay questions and see what the admissions committee wants to hear from each.

MIT Essay Prompt #1

This MIT essay prompt is very broad. The structure of the prompt indicates that the committee is interested in learning about your curiosity inside and outside of the classroom, so don't feel like you have to write about your favorite parts of school.

This MIT essay is your opportunity to show a different side of your personality than the admissions committee will see on the rest of your application. This essay is your chance to show yourself as a well-rounded person who has a variety of different interests and talents.

Choose a specific activity here. You don't need to present a laundry list of activities—simply pick one thing and describe in detail why you enjoy it. You could talk about anything from your love of makeup tutorials on YouTube to the board game nights you have with your family. The key here is to pick something that you're truly passionate about.

Don't feel limited to interests relating to your potential major. MIT's second prompt is all about that, so in this first prompt forget about what the school "wants to read" and be yourself! In fact, describing your experience in or passion for a different field will better show that you're curious and open to new ideas.

MIT Prompt #2

Don't repeat information that the committee can find elsewhere on your application. Take the time to share fun, personal details about yourself.

For instance, do you make awesome, screen-accurate cosplays or have a collection of rock crystals from caving expeditions? Think about what you love to do in your spare time.

Be specific—the committee wants to get a real picture of you as a person. Don't just say that you love to play video games, say exactly which video games you love and why.

MIT wants to know about your community—the friends, family, teammates, etc. who make up your current life. All of those people have affected you in some way—this prompt is your chance to reflect on that influence and expand on it. You can talk about the deep bonds you have and how they have affected you. Showing your relationships to others gives the committee a better idea of how you will fit in on MIT's campus.

All in all, this MIT essay is a great opportunity to have some fun and show off some different aspects of your personality. Let yourself shine!

MIT Prompt #3

This MIT prompt is by far the most specific, so be specific in your answer. Pick one experience that's meaningful to you to discuss here. The prompt doesn't specify that you have to talk about something academic or personal. It can be anything that you've done where you have contributed to any community—your dance troupe, gaming friends, debate team teammates. A community can be anything; it doesn't just refer to your hometown, scholastic or religious community.

The trick to answering this prompt is to find a concrete example and stick to it.

Don't, for instance, say that you try to recycle because the environment is meaningful to you, because it won't sound sincere. Rather, you can talk about why picking up garbage in the park where you played baseball as a child has deeper meaning because you're protecting a place that you've loved for a long time. You should talk about something that is uniquely important to you, not the other thousands of students that are applying to MIT.

Pick something that is really meaningful to you. Your essay should feel sincere. Don't write what you think the committee wants to hear. They'll be more impressed by a meaningful experience that rings true than one that seems artificial or implausible.

MIT Prompt #4

This question sets you up for success: it targets your area of interest but doesn't pigeon-hole you.

This essay is where your formal education will be most important. They want to know what kind of academic life you may lead in college so keep it brief, but allow your excitement for learning to drive these words. You are, after all, applying to MIT—they want to know about your academic side.

You should demonstrate your knowledge of and affinity for MIT in this essay. Don't just say that you admire the MIT engineering program—explain exactly what it is about the engineering program that appeals to you.

You can call out specific professors or classes that are of interest to you. Doing so helps show that you truly want to go to MIT and have done your research.

If you love playing games with kids at the Boys & Girls Club, the third MIT essay prompt is the time to talk about that passion.

MIT Open-Ended Text Box

This is one of the most open-ended options that you'll find on a college application! Here's one last chance for you to let MIT get to know the real you—the you that didn't quite get to come out during the previous four essays.

MIT wants to know exactly who you are, but, just as a word of caution, make sure your answer is appropriate for general audiences.

How to Write a Great MIT Essay

Regardless of which MIT essay prompt you're responding to, you should keep in mind the following tips for how to write a great MIT essay.

#1: Use Your Own Voice

The point of a college essay is for the admissions committee to have the chance to get to know you beyond your test scores, grades, and honors. Your admissions essays are your opportunity to make yourself come alive for the essay readers and to present yourself as a fully fleshed out person.

You should, then, make sure that the person you're presenting in your college essays is yourself. Don't try to emulate what you think the committee wants to hear or try to act like someone you're not.

If you lie or exaggerate, your essay will come across as insincere, which will diminish its effectiveness. Stick to telling real stories about the person you really are, not who you think MIT wants you to be.

You're the star of the show in your MIT essays! Make sure your work reflects who you are as a student and person, not who you think the admissions committee wants you to be.

#2: Avoid Clichés and Overused Phrases

When writing your MIT essays, try to avoid using clichés or overused quotes or phrases.

These include quotations that have been quoted to death and phrases or idioms that are overused in daily life. The college admissions committee has probably seen numerous essays that state, "You miss a hundred percent of the shots you don't take."  Strive for originality.

Similarly, avoid using clichés, which take away from the strength and sincerity of your work.

Your work should be straightforward and authentic.

#3: Check Your Work

It should almost go without saying, but you want to make sure your MIT essays are the strongest example of your work possible. Before you turn in your MIT application, make sure to edit and proofread your essays.

Your work should be free of spelling and grammar errors. Make sure to run your essays through a spelling and grammar check before you submit.

It's a good idea to have someone else read your MIT essays, too. You can seek a second opinion on your work from a parent, teacher, or friend. Ask them whether your work represents you as a student and person. Have them check and make sure you haven't missed any small writing errors. Having a second opinion will help your work be the best it possibly can be.

#4: Demonstrate Your Love for MIT

MIT's five essay prompts are specific to MIT. Keep that in mind as you're answering them, particularly when you attack prompt two.

Show why MIT is your dream school—what aspects of the education and community there are most attractive to you as a student.

MIT receives thousands of applications, from students who have different levels of interest in the university.

The more you can show that you really want to go to MIT, the more the school will be interested in your application. Your passion for MIT may even give you a leg up on other applicants.

What's Next?

Exploring your standardized testing options? Click here for the full list and for strategies on how to get your best ACT score .

Are you happy with your ACT/SAT score, or do you think it should be higher? Learn what a good SAT / ACT score is for your target schools .

Your MIT essays are just one part of your college application process. Check out our guide to applying to college   for a step-by-step breakdown of what you'll need to do.

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Hayley Milliman is a former teacher turned writer who blogs about education, history, and technology. When she was a teacher, Hayley's students regularly scored in the 99th percentile thanks to her passion for making topics digestible and accessible. In addition to her work for PrepScholar, Hayley is the author of Museum Hack's Guide to History's Fiercest Females.

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What should I go with for MIT essay prompt: "What do you do for fun?"

The MIT application has a question that asks “We know you lead a busy life, full of activities, many of which are required of you. Tell us about something you do for the pleasure of it.”

I’m having trouble deciding between talking about drawing/painting or novel-writing. I have lots of creative writing awards on the national and state level, and lots of literary EC’s as well. My love for writing is going to be a major component of my application.

On the other hand, art won’t appear anywhere else on my application. It’s something I genuinely enjoy, plus I’ve sold some commissions and won very minor awards, but I haven’t taken an art class at school since freshman year and I don’t have any EC’s for it. I don’t know if talking about this is going to provide another angle on who I am, or make me look disorganized/disingenuous.

However, mentioning how I like to write might just be redundant here, since it’s proven over and over in other facets of my application.

Which one should I choose? Or should I go with a completely different topic (badminton, blogging, etc.)?

The only wrong answer would be to write about something that you don’t actually do for fun. Give a shot at both essays and see which one you like better. There’s no definitive answer here.

Anything you want to write about is fine. That is kind of the point. Use it to tell the school something new and interesting about yourself.

How about what you REALLY do for fun rather than what item you think puts you in the best light for your essay reader? Sincerity has a quality all its own. That’s the purpose of this essay – not seeing how you can beef up one of your ECs or traits.

I’d like to clarify that I like to do both of these things for fun. However, I only can choose to submit an essay about one of these things in the end.

Thanks to everybody for the advice, though! I can see how this would be something that depends on each individual applicant, and there’s no “right” or “wrong” answer. I’ll probably write both essays and see.

What makes you laugh and fills you with joy? What gives you stress relief? If it is something silly or quirky that is fine.

The point of the exercise is that YOU answer the prompt…isn’t it? So, use what got you to this point and build on it. You could also use a couple of beginning lines that might lead to a few more words, such as: “It was the funnest of times, it was the worst of times”, or the always popular “Call me Ishmael”.

What are your chances of acceptance?

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5 Marvelous MIT Essay Examples

What’s covered:, essay example #1 – simply for the pleasure of it, essay example #2 – overcoming challenges, essay example #3 – dreams and aspirations, essay example #4 – community at a new school, essay example #5 – community in soccer.

• Where to Get Feedback on Your MIT Essay  

Sophie Alina , an expert advisor on CollegeVine, provided commentary on this post. Advisors offer one-on-one guidance on everything from essays to test prep to financial aid. If you want help writing your essays or feedback on drafts,  book a consultation with Sophie Alina or another skilled advisor.

MIT is a difficult school to be admitted into; a strong essay is key to a successful application. In this post, we will discuss a few essays that real students submitted to MIT, and outline the essays’ strengths and areas of improvement. (Names and identifying information have been changed, but all other details are preserved). 

Read our MIT essay breakdown  to get a comprehensive overview of this year’s supplemental prompts.

Please note: Looking at examples of real essays students have submitted to colleges can be very beneficial to get inspiration for your essays. You should never copy or plagiarize from these examples when writing your own essays. Colleges can tell when an essay isn’t genuine and will not view students favorably if they plagiarized. 

Prompt: We know you lead a busy life, full of activities, many of which are required of you. Tell us about something you do simply for the pleasure of it. 

After devouring Lewis Carrolls’ masterpiece, my world shifted off its axis. I transformed into Alice, and my favorite place, the playground, became Wonderland. I would gallivant around, marveling at flowers and pestering my parents with questions, murmuring, “Curiouser and curiouser.” If Alice’s “Drink Me” potion was made out of curiosity, I drank liters of it. Alice, along with fairytale retellings like the Land of Stories by Chris Colfer, kickstarted my lifelong love of reading. 

Especially when I was younger, reading brought me solace when the surrounding world was filled with madness (and sadly, not like the fun kind in Alice in Wonderland ). There are so many nonsensical things that happen in the world, from shootings at a movie theater not thirty minutes from my home, to hate crimes targeted towards elderly Asians. Reading can be a magical escape from these problems, an opportunity to clear one’s mind from chaos. 

As I got older, reading remained an escape, but also became a way to see the world and people from a new perspective. I can step into so many different people’s shoes, from a cyborg mechanic ( Cinder ), to a blind girl in WWII’s France (Marie-Laure, All the Light We Cannot See ). Sure, madness is often prevalent in these worlds too, but reading about how these characters deal with it helps me deal with our world’s madness, too. 

Reading also transcends generational gaps, allowing me to connect to my younger siblings through periodic storytimes. Reading is timeless — something I’ll never tire of. 

What the Essay Did Well

This essay is highly detailed and, while it plays off a common idea that reading is an escape, the writer brings in personal examples of why this is so, making the essay more their own. These personal examples often include strong language (e.g. “devoured,” “gallivant,” “pestering” ), which make the imagery more vivid, the writing more interesting. More advanced language can add more nuance to an essay– instead of “ate,” the writer chooses to say “devoured, ” and you can almost see the writer taking the book in almost as quickly as they might polish off a tray of cookies. 

The writer also discusses how reading can not only be a solace from events that seem nonsensical, but a way to understand the madness in these events. By giving two different examples of how this can be so, that seem so varied from each other (the cyborg mechanic and the girl in WWII’s France), the writer creates more depth to this idea. 

What Could Be Improved

At the beginning, the writer should consider cutting the introduction paragraph by a line to leave more room for the two major points of the essay in the following paragraphs. Instead of a long sentence about a love of reading being kickstarted, the writer could create a short, powerful sentence to kick off the next two paragraphs. “I was in love with reading.” 

The detail at the end about how reading also transcends generational gaps seems like an add-on that doesn’t connect to the past two ideas– instead, I would suggest that this author expand a little more on the prior two ideas and tie them together at the end. “In this timeless world of reading, I can keep drinking from the well of curiosity. In the pages of a book, I have a space to find out more about the world around me, process its events, and more deeply understand others.”

Prompt: Tell us about the most significant challenge you’ve faced or something important that didn’t go according to plan. How did you manage the situation? 

“It’s… unique,” they say. 

I sag, my younger sister’s koala drawing staring at me from the wall. It always seemed like her art ended up praised and framed, while mine ended up in the trash can when I wasn’t looking. In contrast to my sister, art always came as a bit of a struggle for me. My bowls were lopsided and my portraits looked like demons. Many times, I’ve wanted to scream and quit art once and for all. I craved my parents’ validation, a nod of approval or a frame on the wall. 

Eventually, my art improved, and I made some of my favorite projects, from a ceramic haunted house to mushroom salt-and-pepper shakers. Even then, I didn’t get much praise from my parents, but I realized I genuinely loved art. It wasn’t something I enjoyed because of others’ praise; I just liked creating things of my own and the inexplicable thrill of chasing a challenge. Art has taught me to love failing miserably at something to continue it again the next day. If I never endured countless Bob Ross tutorials, I never would’ve made the mountain painting that I hang in my room today; if I never made pottery that blew up (just once!), I wouldn’t have my giant ceramic pie. 

I’m still light years from being an expert, but I’ll never tire of the kick of a challenge. 

The detail about the sister’s koala drawing being framed and praised while this writer’s portraits look like “demons” and bowls “lopsided” draws a nice contrast between the skills of the sister versus those of the writer.  In response to this “Overcoming Challenges” prompt , the author justifies that this is a significant challenge by saying that they “wanted to scream and quit art once and for all” and that they still desired their parents’ approval. 

The writer’s response to the situation—taking more tutorials online, creating many different pots before getting it right–is nicely framed. Many times, students forget to include examples that demonstrate how they respond to the situation, and this writer does a good job of including some of those details. 

The writer seems to emphasize the parents’ approval piece in the first paragraph, but then moves away from that point more to focus on the “thrill of chasing a challenge.” This essay could be improved by focusing a little more on how the writer emotionally moved past not getting that approval “Even then, I didn’t get much praise from my parents, but I finally realized I didn’t need to focus on that. I could focus on my love of art, on the inexplicable thrill of chasing the challenge…” 

Additionally, the sentence that starts with “Eventually, my art improved…” leaves the reader with the ques tion– how? Saying something like “Eventually, after many YouTube tutorials and a few destroyed pots, my art improved” would add detail, without taking away from the sentence about the Bob Ross tutorials and the pot blowing up. 

Prompt: Describe the world you come from (for example, your family, school, community, city, or town). How has that world shaped your dreams and aspirations? (225 words)

When the school bell rang, I jumped on my bike and sped home to watch the Tom and Jerry cartoon. I took off my school uniform and sat in the living room, pressing the remote’s power button. I pressed it again frantically, feeling another heart drop as the screen remained black. “Oh my God,” I sobbed as I rushed up to ensure that all wires were properly plugged into their respective sockets, but the screen was still black.

I unplugged the television, disassembled it, and examined every component, starting with the power switch. I’ve been tinkering with old radios for a long time, so I easily realized that a power surge had destroyed its capacitor. I replaced it with one from my radio, and the TV turned on immediately. While I couldn’t watch the cartoon, fixing the TV not only made me happy, but it also piqued my interest in the digital world. I began looking into technical opportunities in my community, starting with a nearby repairing shop, where I became acquainted with electronic devices: smart phones, laptops, televisions, and printers. Today, if I’m not repairing people’s electronics, I’m amazed by integrating broken gadgets.

This writer does an excellent job of addressing the “dreams and aspirations” line of this prompt. They clearly describe how their interest in technology emerged, in a well-paced, energetic way that makes us readers vicariously feel their passion and excitement.

Additionally, introducing us to their love of repairing electronics through the seemingly mundane event of their TV not working is a smart choice, for two reasons:

• Everyone has experienced their TV, phone, laptop, etc. not working, so this story helps readers relate to the student
• They go on to talk about how they used their newfound skill to help others, and thus portray themselves as someone who views even the simplest occurrence as an opportunity to make the world better

Obviously, one of the main goals of the college essay is to connect with admissions officers, to get them personally invested in your story and, by extension, excited about your potential as an MIT student. Being relatable is one of the best ways to build that connection.

And, of course, MIT wants to accept students who are going “to advance knowledge and educate students in science, technology, and other areas of scholarship that will best serve the nation and the world in the 21st century” (per MIT’s mission statement). At such a selective school, grades and test scores alone won’t set you apart–aligning your values with theirs is critical, and this student does so in a natural, authentic way.

While this essay is well-written, there’s one major issue: the student doesn’t fully answer the prompt. As noted above, they focus primarily on the “dreams and aspirations” line, and while they do tell a compelling story, we don’t learn anything about their “ family, school, community, city, or town,” beyond a brief mention of a repair shop where they live.

Especially at highly selective schools like MIT, admissions officers choose their essay questions carefully, based on the information they feel they need to properly evaluate your candidacy. So, if you fail to answer part of a prompt, in a certain sense your application is incomplete. As you work towards the final draft of your essay, make sure you reread the prompt and confirm you’ve responded to it thoroughly.

In this essay, for example, the writer could have reworked the opening paragraph slightly, to include details about who they typically watch TV with, whether that’s their friends, siblings, parents, or someone else. They also could have gone into more detail about the repair shop, by describing what their boss was like, if they had any coworkers, and so on. These additions would make the student’s “world” come alive in a way it currently doesn’t. 

Of course, fully responding to a prompt while staying under the word count can be hard, but this student actually has 31 extra words at their disposal. And even if they had to make cuts elsewhere, answering all parts of a prompt takes priority over including every single detail in your story.

Finally, on a linguistic level, the ending of this essay is quite abrupt. In a relatively short supplement like this, you don’t need (or even want) a lengthy conclusion, but you should have a quick line or phrase to wrap up the story. 

For example, say the last line read something like “ Today, if I’m not repairing people’s electronics, I’m amazed by integrating broken gadgets, and dreaming of all the fixes I have yet to learn.” With just a few extra words, this version not only brings things full circle by connecting back to the prompt, but also subtly builds a bridge between the student’s current passions and their potential future at MIT.

Prompt: MIT brings people with diverse backgrounds and experiences together to better the lives of others. Our students work to improve their communities in different ways, from tackling the world’s biggest challenges to being good friends. Describe one way you have collaborated with people who are different from you to contribute to your community. (225 words)

Embarking in a new environment can be challenging, but when everyone is new, it can be disastrous. After completing grade 9, every Rwandan student is transferred to a new school to pursue advanced secondary schooling. When I transferred to a new school, people only talked to those who had previously attended the same school, resulting in fierce competition and people being unable to interact together.

In an effort to solve this problem, I brainstormed ways to bring the entire class together, and “The caremate game” came to mind. I assigned each student a caretaker, another student with whom they were unfamiliar, and required them to look after him / her for the entire week, which included telling stories, buying snacks in the canteen, jogging together, and so on. However, because some people would not accept this game in the first place, I spoke to the tastemakers in the class before introducing it so that they could persuade others.

Everything went as planned; some students who couldn’t even interact before ended up in relationships. Everyone wanted to play it again, and we ended up doing so three times. Today, we are no longer divided; rather, we are a family of brothers and sisters.

In this essay, which is responding to a creative take on the classic “Community” prompt, the student does a great job of showcasing several qualities that MIT prizes in its students: problem-solving, imagination, and empathy, as well as an ability to make a difference in their everyday life.

We also want to highlight that the same student actually wrote both this essay and Example #4. We point this out because these two essays work in tandem, to present the student as simultaneously inventive and altruistic, even in quite ordinary situations. This picture would not be as clear if the student has chosen to highlight one set of qualities in Example #4, and a different set here.

Because college applications are inherently limiting in how much information they allow you to share about yourself–nobody can pack their entire personality into a transcript, an activities list, a 650-word personal statement, and a handful of supplements–some students are tempted to pack as much about themselves into their supplements as possible. However, that approach typically ends up being counterproductive.

Of course, we are all multifaceted, but if you choose to present yourself in three different ways in three different essays, MIT admissions officers may be unclear on who exactly they’d be admitting to their school. Remember, they’re trying to determine not just how well you’d do at MIT yourself, but also how you’d fit into the broader freshman class they’re assembling.

For example, if you were to talk about your love of fixing electronics above and then, say, your Taylor Swift fandom here, admissions officers may have a hard time determining how those two pieces of your personality fit together. After all, they have no additional background context on you, and they also have no choice but to read applications quickly, because they have so many to get through. 

So, while having different, seemingly conflicting sides to your personality is part of being human, in the context of college applications specifically your goal should be to emphasize the same points in each essay, like this student. Cohesive applications are more memorable to admissions officers, as they clearly and directly show what that student has to offer that nobody else in the applicant pool does.

Just like there is some overlap between the strengths of this student’s essays, this essay’s biggest fault is a disconnect with the prompt, which asks applicants to “Describe one way you have collaborated with people who are different from you to contribute to your community.”

Remember, MIT admissions officers choose their prompts carefully. Including the bolded line, rather than having the prompt be just “Describe one way you have contributed to your community” means they want to see collaboration highlighted in your response.

While this student briefly mentions speaking “ to the tastemakers in the class before introducing it so that they could persuade others,” this is the only mention of collaboration in the essay, and we get no detail about what their conversations with these other students looked like, or the specific actions the other students took to ensure the success of the project. When a topic features so prominently in the prompt, you want to make sure you give it more than a passing glance in your response.

We also don’t get any explanation of what made these students different from the author. We can infer that they didn’t go to the same school before, but you never want to leave a key detail up to inference, as it’s always possible your reader doesn’t read you the way you intend. 

Additionally, going to a different school doesn’t tell us what made these students different on a deeper level. MIT wants to see that you’re prepared to thrive at a school with students from all corners of the world, some of whom will have drastically different life experiences from you. Because we don’t know what made this student different from their peers in terms of personality, background, etc., nor how they worked across that difference, we can’t envision how they’d navigate MIT’s diverse student body.

Overall, the takeaway here is that choosing a topic for a college essay is a two-fold process. First, you need to have a strong story, which this student does. But secondly, and just as importantly, you want to cater the details of that story to the specific prompt you’re responding to, so that admissions officers will have all the information they need to make a well-informed decision about your candidacy.

Prompt: At MIT, we bring people together to better the lives of others. MIT students work to improve their communities in different ways, from tackling the world’s biggest challenges to being a good friend. Describe one way in which you have contributed to your community, whether in your family, the classroom, your neighborhood, etc. (200-250 words)

“Orange throw!”

As I extended my arm to signal properly, the smallest girl on the orange team picked up the ball to throw it back into play. In AYSO, U10 players often lift their back foot when throwing the ball, so I focused my attention there.

Don’t lift it. Keep it down.

It shot straight up. 

My instincts blew the whistle to stop the game. The rulebook is simple: the rule was broken, give it to the other team. But the way she tried, eager to play, eager to learn and try again— I couldn’t punish that. So I made my way over to the sideline to try it myself.

“When we’re throwing it in, we wanna keep our back foot down. Try again!” After demonstrating, I backpedaled a bit and watched her throw again.

Don’t lift it. Keep it down… Ah, it stayed down.

“Nice throw!”

And just like that, we were off again. These short, educational encounters happen multiple times a game. And while they may not be prescribed, they provide so many learning opportunities. These kids, they’re the future of soccer. If they learn the basics, they can achieve greatness.

Every time I step out onto the pitch, that’s what I see: potential. Little Alex may not throw correctly now, but with work, she could become the next Alex Morgan. That’s why, in every soccer game I referee, every new situation I’m thrust into, I strive to see what’s more; I strive to see the potential.

There is so much imagery in this essay! It’s easy to see the scene in your mind. Through details such as “smallest girl” and describing the team as the “orange,” the reader can more easily picture the scene in their mind. Giving color, size, and other details such as these can make the imagery stronger and the picture clearer in the reader’s mind. 

The writer narrates their thought process through their use of italics, bringing the reader into the mind of the writer. The space for each line of dialogue separates each thought, so that the reader can feel the full emphasis of each line. The mingling of cognitive narration and details about the setting keep the momentum of the essay. 

Through this essay, we learn that this referee is supportive to the members of the youth soccer teams that they are refereeing; instead of seeing the role of referee as punitive (punishing), this writer sees it as a coaching experience. This idea of creating educational encounters as one’s contribution to the community is definitely a great idea to build upon for this essay prompt. 

The contribution to the community is clear because of the emphasis on the coaching aspect of refereeing. However, especially thinking about structure, the author spends about half the essay on a single situation. Limiting this story to a third of the essay could give the writer more space to provide examples of other ways that the author has coached others. The author could have also connected this coaching experience to a mentoring experience in a different context, such as mentoring students at the YMCA,  to create more connections between other extracurriculars and give more weight to this author’s contributions to the community. 

The second to last paragraph ( “And just like that, we were off again…” ) could benefit from another example or two about showing, not telling. The sentence “And while they might not be prescribed, they provide so many learning opportunities” is already clear from the situation that the author has given; the author has already called these “educational encounters” in the prior sentence. Instead of that sentence, the writer could have given another example about a child thanking the writer for a coaching tip, or the expression on a different player’s face when they learned a new skill. 

Additionally, the role of the writer is not immediately clear at the beginning, although it’s suspected that this student is most likely the referee. The writer also provides details about “AYSO” (American Youth Soccer Organization) and “U10,” where they could have simply referred to the games as “youth soccer games” to get the point across that the players are still learning basic skills about throwing the ball in. 

To make all of this clear, the writer could have said “As a referee for youth soccer games, I have seen that players often lift their back foot when throwing the ball, so I focused my attention there.” Acronyms are usually best to be avoided in essays- they can take the reader’s attention away from what is actually happening and lead them to wonder about what the letters in the acronym stand for.

Where to Get Feedback on Your MIT Essay 

Do you want feedback on your MIT  essays? After rereading your essays countless times, it can be difficult to evaluate your writing objectively. That’s why we created our free Peer Essay Review tool , where you can get a free review of your essay from another student. You can also improve your own writing skills by reviewing other students’ essays. 

If you want a college admissions expert to review your essay, advisors on CollegeVine have helped students refine their writing and submit successful applications to top schools. Find the right advisor for you to improve your chances of getting into your dream school!

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• What You Do For Fun

We know you lead a busy life, full of activities, many of which are required of you. Tell us about something you do simply for the pleasure of it.

I love listening to hard rock and heavy metal music. I find these music genres liberating because they pump me up and help me release stress. I enjoy doing this so much that I am an expert at games such as Guitar Hero and Rock Band, which I play with friends or alone just for the pleasure of it. Music allows me to entertain myself when I have to do things I do not like to do. Between rock music and reading a good book I can spend a full afternoon or evening perfectly content.

Essays That Worked

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Victor G Negron

Mathematics, graduated mit '15, more mit essays.

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46 Essays that Worked at MIT

Updated for the 2024-2025 admissions cycle.

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The Massachusetts Institute of Technology (MIT) is a world-renowned research university based in Cambridge, Massachusetts. Known for its prioritization of intellectual freedom and innovation, MIT offers students an education that’s constantly on the cutting-edge of academia. The school’s star-studded roster of professors includes Nobel prize winners and MacArthur fellows in disciplines like technology, biology, and social science. A deeply-technical school, MIT offers students with the resources they need to become specialists in a range of STEM subjects. In many ways, MIT is the gold standard for creativity, critical thinking, and problem solving.

Unique traditions at MIT

1. "Ring Knocking": During the weeks preceding the MIT Commencement Ceremony, graduating students celebrate by finding a way to touch the MIT seal in the lobby of Building 10 with their newly-received class rings. 2. "Steer Roast": Every year in May, the MIT Science Fiction Society hosts a traditional event on the Killian Court lawn for incoming freshmen. During the Steer Roast, attendees cook (and sometimes eat) a sacrificial male cow and hang out outside until the early hours of the morning. 3. Pranking: Pranking has been an ongoing tradition at MIT since the 1960s. Creative pranks by student groups, ranging from changing the words of a university song to painting the Great Dome of the school, add to the quirkiness and wit of the MIT culture. 4. Senior House Seals: The all-senior undergraduate dormitory of Senior House is known for its yearly tradition of collecting and displaying seals, which are emblems that represent the class of the graduating seniors.

Programs at MIT

1. Global Entrepreneurship Lab (G-Lab): G-Lab provides undergraduate and graduate students with the skills to build entrepreneurial ventures that meet developing world challenges. 2. Mars Rover Design Team: This club is part of the MIT Student Robotics program that provides students with the engineering, design, and fabrication skills to build robots for planetary exploration. 3. Media Lab: The Media Lab is an interdisciplinary research lab that explores new technologies to allow individuals to create and manipulate communication presentation of stories, images, and sounds. 4. Independent Activities Period (IAP): A month-long intersession program that allows students to take courses and participate in extracurricular activities from flying classes to volunteering projects and sports. 5. AeroAstro: A club that provides students with the opportunity to learn about aerospace engineering and build model rockets.

At a glance…

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Cambridge, MA

Real Essays from MIT Admits

Prompt: mit brings people with diverse backgrounds and experiences together to better the lives of others. our students work to improve their communities in different ways, from tackling the world’s biggest challenges to being a good friend. describe one way you have collaborated with people who are different from you to contribute to your community..

Last year, my European History teacher asked me to host weekly workshops for AP test preparation and credit recovery opportunities: David, Michelangelo 1504. “*Why* is this the answer?” my tutee asked. I tried re-explaining the Renaissance. Michelangelo? The Papacy? I finally asked: “Do you know the story of David and Goliath?” Raised Catholic, I knew the story but her family was Hindu. I naively hadn’t considered she wouldn’t know the story. After I explained, she relayed a similar story from her culture. As sessions grew to upwards of 15 students, I recruited more tutors so everyone could receive more individualized support. While my school is nearly half Hispanic, AP classes are overwhelmingly White and Asian, so I’ve learned to understand the diverse and often unfamiliar backgrounds of my tutees. One student struggled to write idiomatically despite possessing extensive historical knowledge. Although she was initially nervous, we discovered common ground after I asked about her Rohan Kishibe keychain, a character from Jojo’s Bizarre Adventure. She opened up; I learned she recently immigrated from China and was having difficulty adjusting to writing in English. With a clearer understanding of her background, I could now consider her situation to better address her needs. Together, we combed out grammar mistakes and studied English syntax. The bond we formed over anime facilitated honest dialogue, and therefore genuine learning.

Essay by Víctor

i love cities <3

Prompt: We know you lead a busy life, full of activities, many of which are required of you. Tell us about something you do simply for the pleasure of it.

I slam the ball onto the concrete of our dorm’s courtyard, and it whizzes past my opponents. ******, which is a mashup of tennis, squash, and volleyball, is not only a spring term pastime but also an important dorm tradition. It can only be played using the eccentric layout of our dorm’s architecture and thus cultivates a special feeling of community that transcends grade or friend groups. I will always remember the amazing outplays from yearly tournaments that we celebrate together. Our dorm’s collective GPA may go significantly down during the spring, but it’s worth it.

Essay by Brian

CS, math, and economics at MIT

Prompt: Describe the world you come from (for example, your family, school, community, city, or town). How has that world shaped your dreams and aspirations?

The fragile glass beaker shattered on the ground, and hydrogen peroxide, flowing furiously like lava, began to conquer the floor with every inch the flammable puddle expanded. This was my solace. As an assistant teacher for a middle school STEM class on the weekends, mistakes were common, especially those that made me mentally pinpoint where we kept the fire extinguishers. However, these mishaps reminded me exactly why I loved this job (besides the obvious luxury of cleaning up spills): every failure was a chance to learn in the purest form. As we conducted chemical experiments or explored electronics kits, I was comforted by the kids’ genuine enthusiasm for exploration—a sentiment often lost in the grade-obsessed world of high school. Accordingly, I tried to help my students recognize that mistakes are often the most productive way to grow and learn. I encouraged my students to persist when faced with failure, especially those who might not have been encouraged in their everyday lives. I was there for students like Nathan, a child on the autism spectrum who reminded me of my older brother with autism. I was there for the two girls in a class of 17, reminding me of my own journey navigating the male-dominated world of STEM. I wanted to encourage them into a lifelong journey of pursuing knowledge and embracing mistakes. I may have been their mentor, but these lessons also serve as a crucial reminder to me that mistakes are not representative of one’s overall worth.

Essay by Sarah J.

cs @ stanford!! lover of STEM, taylor swift, and dogs!

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Someone with the same interests, stats, and background as you

MIT Essays that Worked

Mit essays that worked – introduction.

In this guide, we’ll provide you with several MIT essays that worked. After each, we’ll discuss elements of these MIT essay examples in depth. By reading these sample MIT essays and our expert analysis, you’ll be better prepared to write your own MIT essay. Before you apply to MIT, read on for six MIT essays that worked.

Massachusetts Institute of Technology

The Massachusetts Institute of Technology is a private research university in Cambridge , Massachusetts. Since its founding in 1861, MIT has become one of the world’s foremost institutions for science and technology . With MIT ranking highly year after year, the low MIT acceptance rate is no surprise. Knowing how to get into MIT means knowing about MIT admissions, the MIT application, and how to write MIT supplemental essays.

MIT Supplemental Essay Requirements

The MIT application for 2022–2023 requires four short essays. Each essay should be up to 200 words in length.

MIT essay prompts :

We know you lead a busy life, full of activities, many of which are required of you. tell us about something you do simply for the pleasure of it., describe the world you come from (for example, your family, school, community, city, or town). how has that world shaped your dreams and aspirations.

• MIT brings people with diverse backgrounds and experiences together to better the lives of others. Our students work to improve their communities in different ways, from tackling the world’s biggest challenges to being a good friend. Describe one way you have collaborated with people who are different from you to contribute to your community.
• Tell us about a significant challenge you’ve faced (that you feel comfortable sharing) or something that didn’t go according to plan. How did you manage the situation?

MIT changes the wording of these prompts a little bit every year. As a result, our MIT essay examples may look a little different from the prompts to which you will be crafting your own responses. However, there is a lot of overlap between current and past prompts and often the underlying questions are the same. In other words, even if the prompts differ, most of our MIT essays that worked are still helpful. Even MIT essay examples for prompts that are gone can be useful as a general sample college essay.

As one of the best universities worldwide, MIT is nearly impossible to get into without a good strategy . Even if you don’t have a stellar ACT or SAT score , your essays may impress admissions officers. Let’s briefly analyze each prompt so we know what to look for in MIT essays that worked.

MIT Essay Prompt Breakdown

1. extracurricular essay.

First, you’ll write about an activity you enjoy, whether it’s baking, doing magic tricks, or writing fanfiction. Remember, strong MIT essay examples for this prompt show genuine enthusiasm and explain why the activity is meaningful. Choose a hobby you can write about with gusto while also showing what it means to you.

2. Your Background Essay

Next, we have a prompt asking about your background. This is a classic question; in every other sample college essay, you find answers to this prompt. This question is intentionally open-ended, allowing you to write about any aspect of your background you’d like. In the MIT essays that worked, the “world” has something important to say about the author’s values or outlook.

3. Community Essay

Then, the third essay asks how you work with diverse groups to contribute to a larger community. MIT wants to see that you can work toward community goals while valuing diverse perspectives. But don’t worry. They don’t expect you to have solved world hunger—pick something that demonstrates what community means to you.

4. Significant Challenge Essay

Lastly, we have the failure essay, which seeks to answer how you persist in the face of adversity. Notice the prompt doesn’t mention “overcoming,” so this can be a time that you completely flat-out failed. Everyone handles setbacks differently, so effective MIT essay examples illustrate the author’s unique way of managing failure. It doesn’t have to be a particularly unique or unusual failure, although that may help you stand out .

How to Apply to MIT

MIT doesn’t accept the Common or Coalition Application. Instead, there’s a school-specific application for all prospective students. The 2022 Early Action MIT application deadline was November 1. The Regular Action MIT application deadline is usually January 1, but it’s been extended this year to January 5, 2023. The financial aid information deadline is February 15, 2023.

Depending on your admissions round, you need to submit all materials to the Apply MIT portal by the specified deadline.

MIT application requirements

• Basic biographical information, including your intended area of study
• Four supplemental essays
• A brief list of four extracurricular activities that are meaningful to you
• Self-reported coursework information
• A Secondary School Report from your guidance counselor, including your transcript
• Two letters of recommendation : MIT recommends one from a STEM teacher and one from a humanities teacher.
• SAT or ACT scores —MIT is not test-optional for 2022–2023!
• The February Updates form with your midyear grades (goes live in mid-February)

Furthermore, interviews are offered to many—but not all—students; not being offered an interview doesn’t negatively reflect on your application. At the end of this article, we compile more resources regarding the rest of the application. If you have specific questions about your application, reach out to the MIT admissions office .

Now that we’ve discussed the prompts and MIT admissions process, let’s read some MIT essays that worked. We have six sample MIT essays to help you learn how to write MIT supplemental essays. And, if you’re looking to test your knowledge of college admissions, take our quiz below!

MIT Essay Examples #1 – Cultural Background Essay

The first of our MIT essay examples responds to a prompt that isn’t exactly on this year’s list. Let’s take a look. The prompt for this MIT essay that worked is:

Please tell us more about your cultural background and identity in the space below (100 word limit). If you need more than 100 words, please use the Optional section on Part 2.

Although the wording isn’t identical to any of this year’s prompts, it is similar to prompt #2. Remember, essay prompt #2 asks about the world you come from, which is essentially your background. However, MIT essay examples for this prompt speak more specifically about cultural background. With a shorter word limit, concise language is even more critical in MIT essays that worked for this prompt.

MIT Essays That Worked #1

My dad is black and my mom is white. But I am a shade of brown somewhere in between. I could never wear my mom’s makeup like other girls. By ten, I was tired seeing confused stares whenever I was with my dad. I became frustrated and confused. I talked to my biracial friends about becoming confident in my divergent ancestral roots. I found having both an understanding of black issues in America and of the middle class’ lack of exposure gave me greater clarity in many social issues. My background enabled me to become a compassionate, understanding biracial woman.

Why This Essay Worked

MIT essays that worked effectively show that the author can think about the bigger picture. This author describes their experiences as a biracial woman while addressing the wider scope of racial issues. While you shouldn’t reach to reference irrelevant societal problems, MIT essays that worked do often incorporate big ideas.

In addition, this author mentions conversations with biracial friends. MIT essay examples often include collaboration and community, and this one is no different. Often, sample MIT essays about cultural background will connect that heritage with one’s community. It shows that you value what makes you unique and can find it in others.

Lastly, strong MIT essay examples display reflection and personal growth. Do you understand the ways your experiences have shaped you, and can you write about them? Can you point to areas where you’ve grown as a result of your experiences? MIT essays that worked link the topic and the writer’s personal growth or values.

MIT Essays That Worked #2 – Activities Essay

The second of our MIT essay examples answers a prompt that’s on this year’s list.

In other words, write about a hobby or extracurricular activity—and what it says about you. As we mentioned above, MIT essays that worked for this prompt aren’t all about lofty ambitions. If you don’t read textbooks in your spare time, don’t write an essay claiming that’s your hobby. Be honest, thoughtful, and enthusiastic while finding a way to make your uniqueness show through. Let’s read one of many MIT essays that worked for this prompt.

MIT Essays That Worked #2

Adventuring. Surrounded by trees wider than I am tall on my right and the clear, blue lake on my left. I made it to the top after a strenuous hike and it was majestic. There is no feeling like the harmony I feel when immersing myself in nature on a hike or running through the mud to train for my sprint triathlon or even fighting for a pair of cute boots on black Friday. I take pleasure in each shade of adventure on my canvas of life, with each deliberate stroke leading me to new ideas, perspectives, and experiences.

MIT essays that worked use precise language to appeal to readers’ emotions. Note words like “strenuous,” “majestic,” “harmony,” and “deliberate.” The strategic use of vivid words like this can strengthen MIT essay examples and heighten their impact. But don’t overuse them—like paintings use a variety of shades, you should play with the intensity of your words.

Another benefit of colorful language is conveying meaning more deeply and precisely. Well-written MIT essay examples layer on meaning: this author likes adventuring through nature as well as life. With effective diction, you can make the most of the words you’re given. Consider using metaphors like in this MIT essay conclusion, comparing life to a canvas.

Now, think about your impression of the author after reading this. They’re active, ambitious, and, above all, adventurous. We know they like to challenge themselves (training for a triathlon) but also like fashion (buying cute boots). And we see from their concluding sentence that they have no intention of slowing down or pulling back. In under 100 words, we’ve got a clear snapshot of their worldview and see their adventuring spirit fits MIT.

MIT Essay Examples #3 – Why Major Essay

The third of our MIT essays that worked answers a prompt that isn’t on our list for 2022.

Although you may not yet know what you want to major in, which department or program at MIT appeals to you and why?

This is a classic “Why Major” essay, asked by hundreds of colleges every year. Obviously, the prompt asks about your academic interests . However, it subtly asks about school fit : why is MIT the best place for you to pursue this interest? Although this sample college essay prompt isn’t in this cycle, you should read as many sample MIT essays as possible. MIT essays that worked for the “Why Major” essay prompt illustrated the author’s academic interests and motivations. Let’s see what the next of our sample MIT essays has to say.

MIT Essays That Worked #3

My first step in to the Koch Institute for Integrative Cancer Research was magical. My eyes lit up like Christmas lights and my mind was racing faster than Usain Bolt. I was finally at home, in a community where my passions for biology, chemistry, math, and engineering collided, producing treatments to save lives everywhere.

I pictured myself in a tie-dyed lab coat, watching a tumor grow in a Petri disk then determining my treatment’s effectiveness. If I am admitted to MIT, I look forward to majoring in bioengineering and shaping and contributing to the forefront of bioengineering research.

Earlier, we said that MIT essays that worked use vivid language to drive home their point. This sample college essay is no different. Describing their instantaneous reaction, the author pulls us into their headspace to share in their delight. Following that, they show us their vision for the future. Finally, they state directly how they’ll work toward that vision at MIT.

This author points out that bioengineering aligns with their interests across math and the sciences. There’s no rule saying you can’t be purely into math, but MIT strives to cultivate the world’s leading minds. Many MIT essays that worked present the author as a multifaceted person and intellectual. If you write a Why Major essay for a STEM field, it may be worth your while to take an interdisciplinary angle.

Among other parts of these MIT essays that worked in the author’s favor is the mention of an experience. Many model MIT essay examples directly reference the author’s life experiences to connect them with their interest. For instance, this author frames their essay with a visit to a cancer research institute. We don’t know if it’s a tour or an internship—the reason for their visit is less important than the impact.

MIT Essay Examples #4 – Community Essay

At this point, we’ve gone through half of our MIT essay examples. Moving on, we’ll read three MIT essays that worked for prompts (nearly) identical to this year’s. Next, we’ve got a prompt asking about community contributions.

At MIT, we bring people together to better the lives of others. MIT students work to improve their communities in different ways,  from tackling the world’s biggest challenges to being a good friend. Describe one way in which you have contributed to your community, whether in your family, the classroom, your neighborhood, etc.

It’s very similar to this year’s third prompt, with one crucial difference. The current prompt asks for “one way you have collaborated with people who are different from you .” While past MIT essay examples for this prompt could have focused on individual efforts, now you should focus on group efforts. In particular, groups where “people who are different from you” also play key roles. This is intentionally open-ended, allowing for endless kinds of differences.

With that said, let’s continue with our MIT essay examples.

MIT Essays That Worked #4

“I’m going to Harvard,” my brother proclaimed to me. My jaw dropped. My little brother, the one who I taught to pee in the toilet, the one who played in the pool with me every day of the summer for 7 years, the one who threw me in the trash can 3 months ago, had finally realized the potential I have seen in him since he was a little kid. And I was thrilled.

He told me that after attending the Harvard basketball program, he knew that attending college was the perfect opportunity for him to continue playing the sport he loved as well as get a very good education. His end goal (this is where I almost cried) was to become an engineer at Nike. The best part, though, is that he asked me to help him achieve it. 

I was astounded that he thought so highly of me that he trusted me to help him. That night, we began discussing various fields of engineering that he could pursue, as well as the internship opportunities that he classified as “so cool.” As soon as school started, I bought him a planner and taught him to keep his activities organized. I go over homework with him and my baby brother almost every night.

I love using my knowledge to contribute to my family with my knowledge. I am so proud of my brother and our progress. I cannot wait to see him grow as he works to achieve his dream.

Perhaps while reading the prompt, you thought all MIT essays that worked discussed setting up a food bank or working at a hospital. Not so! What really matters for this essay is the impact the community has on you. In sample MIT essays like this one, we see just how important the writer’s family is to them. If your family means the world to you, don’t shy away from writing about them!

On the other hand, while many sample MIT essays discuss family, the best ones remember to center the author. It may seem selfish, but in an applicant pool of over 30,000 , you must stand out. You have to beat that low MIT acceptance rate by putting your best foot forward. Notice how the author’s feelings and thoughts show through in their interactions and reactions. Even in recounting their past with their little brother, you see them as a caring, playful older sibling. They’re thoroughly proud of their brother, his ambitions, and the trust he’s placed in them.

MIT Essay Examples #5 – Describe Your World 

The fifth of our MIT essay examples answers a prompt in circulation this year. Hooray!

This “world” is open-ended to allow writers to explore the communities and people that have shaped them. This essay calls for deep introspection; can you find a common thread connecting you to your “world”? Some MIT essays that worked discuss family traditions, other city identities, etc. Whatever you choose, it should reflect who you are now and who you want to become.

MIT Essays That Worked #5

I was standing on the top row of the choir risers with my fellow third graders. We were beside the fourth graders who were beside the fifth graders. My teacher struck the first chords of our favorite song and we sang together, in proud call and response “Ujima, let us work together. To make better our community. We can solve! Solve our problems with collective work and responsibility.”

Then the students playing African drums and the xylophones on the floor began the harmonious percussion section and we sang again with as much passion as nine-year-olds can muster. This was my world. As a child, my community was centered around my school. At my school we discovered that if you love something enough, and work hard enough for it, you can do great things for both yourself and others around you.

In the years since I left, I reflected back on the lessons I learned at school. I determined I wanted to focus on the things I love – mathematics, science, and helping others. I also want to harmonize my abilities with those of other people so that we can work together to make the world a better place. Today I aspire to work in integrative research as a bioengineer to address the pressing medical issues of today.

For those who don’t know, ujima is the Swahili word for collective work and responsibility. The most well-crafted MIT essay examples employ narrative devices like framing and theme to leave a lasting impression. This essay, for example, introduces ujima with the choir scene—which itself is collective work—then reflects on the general concept. In every sentence, this writer works with the idea of collaboration and the positive power of the collective.

Among sample MIT essays, this can be challenging if you haven’t thought critically about your past and present. This writer clearly values collective responsibility and sees their future through that lens. They speak directly to their interests and their aspirations of bioengineering. All in all, they show careful consideration of ideas that have influenced them and the direction they want to take.

MIT Essay Examples #6 – Significant Challenge

The last of our MIT essays that worked answers a prompt nearly identical to one from this year.

Tell us about the most significant challenge you’ve faced or something important that didn’t go according to plan. How did you manage the situation? 

The only difference is that this year’s prompt indicates you should feel comfortable sharing what you write about. This seems obvious, but you may be surprised how many students dredge up traumatic experiences in sample college essays. The issue isn’t that these experiences are unpleasant to read; on the contrary, they may be painful to write about. Although many MIT sample essays are somewhat vulnerable, you don’t have to write about experiences you’d rather keep to yourself.

With that said, let’s read the last of our MIT essay examples.

*Please be advised that the following essay example contains discussions of anxiety and panic attacks. 

Mit essays that worked #6.

Ten o’clock on Wednesday, April 2016. Ten o’clock and I was sobbing, heaving, and gasping for air. Ten o’clock and I felt like all my hard work, passion, and perseverance had amounted to nothing and I was not enough. It was ten o’clock on a Wednesday, but it all started in August of 2015. I moved cities in August 2015. I knew the adjustment would be hard, but I thought if I immersed myself in challenging activities and classes I loved, I would get through the year just fine.

I was wrong. With each passing month I experienced increased anxiety attacks, lack of satisfaction in any and every activity, and constant degradation of my personal happiness. By April, I was broken. Naked, bent over the toilet, sweating, shaking, choking on the tightening of my own throat, thinking “not enough, not enough, not enough.” 

It was extremely challenging to pick myself up after such a hard fall. When I finally made it out of the bathroom, I crawled to my room and read “Still I Rise” by Maya Angelou. Her struggle encouraged me to rise to this challenge stronger than I had been before. I prioritized my own happiness and fulfillment, taking care of my body and mind.

I finally realized I did not have to do everything on my own, and began collaborating with my peers to finish the year strong and begin initiatives for the next year. I became a stronger, more confident woman than ever before.

Now, you may understand why this year’s wording includes “that you feel comfortable sharing.” While the author’s vivid description helps immerse us in the moment, a reader may hope they’re okay now. Again, you don’t need to strictly avoid traumatizing moments—but don’t feel obligated to share anything you don’t want to. In any case, the diction is indeed very precise and helps convey just how shaken the author was.

Furthermore, we see how the author dealt with this challenge: they were inspired by Maya Angelou. This ability to seek and find strength beyond yourself is crucial, especially in an ever more connected world. At the end of the essay, the writer notes how they’ve changed by working with others to accomplish goals. Their renewed confidence has made them even stronger and more willing to face challenges.

MIT Essay Examples – Key Takeaways

So after reading six sample MIT essays, what do you think? What are the takeaways from these MIT essays that worked? It goes without saying that you should read more sample MIT essays if you can. Additionally, when you draft your own MIT essays, take time to revise them and have other people read them.

MIT Essays that Worked Takeaways

1. discuss experiences.

The best MIT essay examples keep it real by talking about the author’s experiences. Can you think critically about how they have made you who you are? Find ways to address the prompt with your background and life experiences. You may also find sample MIT essays easier to write when they’re rooted in your reality.

2. Use precise language

Two hundred words are, in fact, not that much space. MIT essays that worked use every word to paint a vivid picture of the writer and their world. Mark Twain said it best: “The difference between the almost right word and the right word is … the difference between the lightning-bug and the lightning.” Choose your words carefully to refine your meaning and strengthen your impact.

3. Reflect on yourself

In college essays, it’s all about you and your personal narrative . So don’t miss any opportunity to introspect on your experiences, community, and personal growth. Demonstrate that you know yourself well enough to point to specific influences on your worldview. We all move through the world in different ways—why do you move the way you do?

4. Be genuine

You’ve heard this a thousand times, and we’ll say it again: be yourself . While you hear all about the typical MIT student and what MIT looks for , we’re all unique individuals. As, or even more, important than good scores or a huge activities list is an accurate representation of you . Write about extracurriculars and subjects and communities that are important to you—not what you think will sound impressive.

Additional MIT Resources from CollegeAdvisor

We have a wealth of resources on how to get into MIT here at CollegeAdvisor.com. We’ve got a comprehensive article on the MIT admissions process, from the MIT acceptance rate to deadlines.

MIT Admissions

Speaking of the acceptance rate, we take a closer look at that, too.

MIT Acceptance Rate

If you’re wondering about MIT tuition and costs, read our breakdown .

MIT Tuition & MIT Cost

Finally, we’ve got a guide covering application strategy from start to finish.

Strategizing Your MIT Application

MIT Essays that Worked – Final thoughts

Placing among the top American universities, we see MIT ranking highly every year, and for good reason. By the same token, it’s very challenging to get admitted. So, in order to get in, you need to know how to write MIT supplemental essays.

We read through several MIT essays that worked and identified strengths in our MIT essay examples. Use these tips when writing your own essays to craft a strong application!

This article was written by  Gina Goosby . Looking for more admissions support? Click  here  to schedule a free meeting with one of our Admissions Specialists. During your meeting, our team will discuss your profile and help you find targeted ways to increase your admissions odds at top schools. We’ll also answer any questions and discuss how  CollegeAdvisor.com  can support you in the college application process.

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What is AI?

Everyone thinks they know but no one can agree. And that’s a problem.

• Will Douglas Heaven archive page

Internet nastiness, name-calling, and other not-so-petty, world-altering disagreements

AI is sexy, AI is cool. AI is entrenching inequality, upending the job market, and wrecking education. AI is a theme-park ride, AI is a magic trick. AI is our final invention, AI is a moral obligation. AI is the buzzword of the decade, AI is marketing jargon from 1955. AI is humanlike, AI is alien. AI is super-smart and as dumb as dirt. The AI boom will boost the economy, the AI bubble is about to burst. AI will increase abundance and empower humanity to maximally flourish in the universe. AI will kill us all.

What the hell is everybody talking about?

Artificial intelligence is the hottest technology of our time. But what is it? It sounds like a stupid question, but it’s one that’s never been more urgent. Here’s the short answer: AI is a catchall term for a set of technologies that make computers do things that are thought to require intelligence when done by people. Think of recognizing faces, understanding speech, driving cars, writing sentences, answering questions, creating pictures. But even that definition contains multitudes.

And that right there is the problem. What does it mean for machines to understand speech or write a sentence? What kinds of tasks could we ask such machines to do? And how much should we trust the machines to do them?

As this technology moves from prototype to product faster and faster, these have become questions for all of us. But (spoilers!) I don’t have the answers. I can’t even tell you what AI is. The people making it don’t know what AI is either. Not really. “These are the kinds of questions that are important enough that everyone feels like they can have an opinion,” says Chris Olah, chief scientist at the San Francisco–based AI lab Anthropic. “I also think you can argue about this as much as you want and there’s no evidence that’s going to contradict you right now.”

But if you’re willing to buckle up and come for a ride, I can tell you why nobody really knows, why everybody seems to disagree, and why you’re right to care about it.

Let’s start with an offhand joke.

Back in 2022, partway through the first episode of Mystery AI Hype Theater 3000 , a party-pooping podcast in which the irascible cohosts Alex Hanna and Emily Bender have a lot of fun sticking “the sharpest needles’’ into some of Silicon Valley’s most inflated sacred cows, they make a ridiculous suggestion. They’re hate-reading aloud from a 12,500-word Medium post by a Google VP of engineering, Blaise Agüera y Arcas, titled “ Can machines learn how to behave? ” Agüera y Arcas makes a case that AI can understand concepts in a way that’s somehow analogous to the way humans understand concepts—concepts such as moral values. In short, perhaps machines can be taught to behave. 

Hanna and Bender are having none of it. They decide to replace the term “AI’’ with “mathy math”—you know, just lots and lots of math.

The irreverent phrase is meant to collapse what they see as bombast and anthropomorphism in the sentences being quoted. Pretty soon Hanna, a sociologist and director of research at the Distributed AI Research Institute, and Bender, a computational linguist at the University of Washington (and internet-famous critic of tech industry hype), open a gulf between what Agüera y Arcas wants to say and how they choose to hear it.

“How should AIs, their creators, and their users be held morally accountable?” asks Agüera y Arcas.

How should mathy math be held morally accountable? asks Bender.

“There’s a category error here,” she says. Hanna and Bender don’t just reject what Agüera y Arcas says; they claim it makes no sense. “Can we please stop it with the ‘an AI’ or ‘the AIs’ as if they are, like, individuals in the world?” Bender says.

It might sound as if they’re talking about different things, but they’re not. Both sides are talking about large language models, the technology behind the current AI boom. It’s just that the way we talk about AI is more polarized than ever. In May, OpenAI CEO Sam Altman teased the latest update to GPT-4 , his company’s flagship model, by tweeting , “Feels like magic to me.”

There’s a lot of road between math and magic.

AI has acolytes, with a faith-like belief in the technology’s current power and inevitable future improvement. Artificial general intelligence is in sight, they say; superintelligence is coming behind it. And it has heretics, who pooh-pooh such claims as mystical mumbo-jumbo.

The buzzy popular narrative is shaped by a pantheon of big-name players, from Big Tech marketers in chief like Sundar Pichai and Satya Nadella to edgelords of industry like Elon Musk and Altman to celebrity computer scientists like Geoffrey Hinton . Sometimes these boosters and doomers are one and the same, telling us that the technology is so good it’s bad .

As AI hype has ballooned, a vocal anti-hype lobby has risen in opposition, ready to smack down its ambitious, often wild claims. Pulling in this direction are a raft of researchers, including Hanna and Bender, and also outspoken industry critics like influential computer scientist and former Googler Timnit Gebru and NYU cognitive scientist Gary Marcus. All have a chorus of followers bickering in their replies.

In short, AI has come to mean all things to all people, splitting the field into fandoms. It can feel as if different camps are talking past one another, not always in good faith.

Maybe you find all this silly or tiresome. But given the power and complexity of these technologies—which are already used to determine how much we pay for insurance, how we look up information, how we do our jobs, etc. etc. etc.—it’s about time we at least agreed on what it is we’re even talking about.

Yet in all the conversations I’ve had with people at the cutting edge of this technology, no one has given a straight answer about exactly what it is they’re building. (A quick side note: This piece focuses on the AI debate in the US and Europe, largely because many of the best-funded, most cutting-edge AI labs are there. But of course there’s important research happening elsewhere, too, in countries with their own varying perspectives on AI, particularly China.) Partly, it’s the pace of development. But the science is also wide open. Today’s large language models can do amazing things . The field just can’t find common ground on what’s really going on under the hood .

These models are trained to complete sentences. They appear to be able to do a lot more—from solving high school math problems to writing computer code to passing law exams to composing poems. When a person does these things, we take it as a sign of intelligence. What about when a computer does it? Is the appearance of intelligence enough?

These questions go to the heart of what we mean by “artificial intelligence,” a term people have actually been arguing about for decades. But the discourse around AI has become more acrimonious with the rise of large language models that can mimic the way we talk and write with thrilling/chilling (delete as applicable) realism.

We have built machines with humanlike behavior but haven’t shrugged off the habit of imagining a humanlike mind behind them. This leads to over-egged evaluations of what AI can do; it hardens gut reactions into dogmatic positions, and it plays into the wider culture wars between techno-optimists and techno-skeptics.

Add to this stew of uncertainty a truckload of cultural baggage, from the science fiction that I’d bet many in the industry were raised on, to far more malign ideologies that influence the way we think about the future. Given this heady mix, arguments about AI are no longer simply academic (and perhaps never were). AI inflames people’s passions and makes grownups call each other names.

“It’s not in an intellectually healthy place right now,” Marcus says of the debate. For years Marcus has pointed out the flaws and limitations of deep learning, the tech that launched AI into the mainstream, powering everything from LLMs to image recognition to self-driving cars. His 2001 book The Algebraic Mind argued that neural networks, the foundation on which deep learning is built, are incapable of reasoning by themselves. (We’ll skip over it for now, but I’ll come back to it later and we’ll see just how much a word like “reasoning” matters in a sentence like this.)

Marcus says that he has tried to engage Hinton—who last year went public with existential fears about the technology he helped invent—in a proper debate about how good large language models really are. “He just won’t do it,” says Marcus. “He calls me a twit.” (Having talked to Hinton about Marcus in the past, I can confirm that. “ChatGPT clearly understands neural networks better than he does,” Hinton told me last year.) Marcus also drew ire when he wrote an essay titled “Deep learning is hitting a wall.” Altman responded to it with a tweet : “Give me the confidence of a mediocre deep learning skeptic.”

At the same time, banging his drum has made Marcus a one-man brand and earned him an invitation to sit next to Altman and give testimony last year before the US Senate’s AI oversight committee.

And that’s why all these fights matter more than your average internet nastiness. Sure, there are big egos and vast sums of money at stake. But more than that, these disputes matter when industry leaders and opinionated scientists are summoned by heads of state and lawmakers to explain what this technology is and what it can do (and how scared we should be). They matter when this technology is being built into software we use every day, from search engines to word-processing apps to assistants on your phone. AI is not going away. But if we don’t know what we’re being sold, who’s the dupe?

“It is hard to think of another technology in history about which such a debate could be had—a debate about whether it is everywhere, or nowhere at all,” Stephen Cave and Kanta Dihal write in Imagining AI , a 2023 collection of essays about how different cultural beliefs shape people’s views of artificial intelligence. “That it can be held about AI is a testament to its mythic quality.”

Above all else, AI is an idea—an ideal—shaped by worldviews and sci-fi tropes as much as by math and computer science. Figuring out what we are talking about when we talk about AI will clarify many things. We won’t agree on them, but common ground on what AI is would be a great place to start talking about what AI should be .

What is everyone really fighting about, anyway?

In late 2022, soon after OpenAI released ChatGPT , a new meme started circulating online that captured the weirdness of this technology better than anything else. In most versions , a Lovecraftian monster called the Shoggoth, all tentacles and eyeballs, holds up a bland smiley-face emoji as if to disguise its true nature. ChatGPT presents as humanlike and accessible in its conversational wordplay, but behind that façade lie unfathomable complexities—and horrors. (“It was a terrible, indescribable thing vaster than any subway train—a shapeless congeries of protoplasmic bubbles,” H.P. Lovecraft wrote of the Shoggoth in his 1936 novella At the Mountains of Madness .)  

For years one of the best-known touchstones for AI in pop culture was The Terminator , says Dihal. But by putting ChatGPT online for free, OpenAI gave millions of people firsthand experience of something different. “AI has always been a sort of really vague concept that can expand endlessly to encompass all kinds of ideas,” she says. But ChatGPT made those ideas tangible: “Suddenly, everybody has a concrete thing to refer to.” What is AI? For millions of people the answer was now: ChatGPT.

The AI industry is selling that smiley face hard. Consider how The Daily Show recently skewered the hype, as expressed by industry leaders. Silicon Valley’s VC in chief, Marc Andreessen: “This has the potential to make life much better … I think it’s honestly a layup.” Altman: “I hate to sound like a utopic tech bro here, but the increase in quality of life that AI can deliver is extraordinary.” Pichai: “AI is the most profound technology that humanity is working on. More profound than fire.”

Jon Stewart: “Yeah, suck a dick, fire!”

But as the meme points out, ChatGPT is a friendly mask. Behind it is a monster called GPT-4, a large language model built from a vast neural network that has ingested more words than most of us could read in a thousand lifetimes. During training, which can last months and cost tens of millions of dollars, such models are given the task of filling in blanks in sentences taken from millions of books and a significant fraction of the internet. They do this task over and over again. In a sense, they are trained to be supercharged autocomplete machines. The result is a model that has turned much of the world’s written information into a statistical representation of which words are most likely to follow other words, captured across billions and billions of numerical values.

It’s math—a hell of a lot of math. Nobody disputes that. But is it just that, or does this complex math encode algorithms capable of something akin to human reasoning or the formation of concepts?

Many of the people who answer yes to that question believe we’re close to unlocking something called artificial general intelligence , or AGI, a hypothetical future technology that can do a wide range of tasks as well as humans can. A few of them have even set their sights on what they call superintelligence , sci-fi technology that can do things far better than humans. This cohort believes AGI will drastically change the world—but to what end? That’s yet another point of tension. It could fix all the world’s problems—or bring about its doom. 

kinda mad how the so called godfathers of AI managed to convince seemingly smart people within AI field & many regulators to buy into the absurd idea that a sophisticated curve fitting (to a dataset) machine can have the urge to exterminate humans — Abeba Birhane (@Abebab) June 30, 2024

Today AGI appears in the mission statements of the world’s top AI labs. But the term was invented in 2007 as a niche attempt to inject some pizzazz into a field that was then best known for applications that read handwriting on bank deposit slips or recommended your next book to buy. The idea was to reclaim the original vision of an artificial intelligence that could do humanlike things (more on that soon).

It was really an aspiration more than anything else, Google DeepMind cofounder Shane Legg, who coined the term, told me last year: “I didn’t have an especially clear definition.”

AGI became the most controversial idea in AI . Some talked it up as the next big thing: AGI was AI but, you know, much better . Others claimed the term was so vague that it was meaningless.

“AGI used to be a dirty word,” Ilya Sutskever told me, before he resigned as chief scientist at OpenAI.

But large language models, and ChatGPT in particular, changed everything. AGI went from dirty word to marketing dream.

Which brings us to what I think is one of the most illustrative disputes of the moment—one that sets up the sides of the argument and the stakes in play. 

Seeing magic in the machine

A few months before the public launch of OpenAI’s large language model GPT-4 in March 2023, the company shared a prerelease version with Microsoft, which wanted to use the new model to revamp its search engine Bing.

At the time, Sebastian Bubeck was studying the limitations of LLMs and was somewhat skeptical of their abilities. In particular, Bubeck—the vice president of generative AI research at Microsoft Research in Redmond, Washington—had been trying and failing to get the technology to solve middle school math problems. Things like: x – y = 0; what are x and y ? “My belief was that reasoning was a bottleneck, an obstacle,” he says. “I thought that you would have to do something really fundamentally different to get over that obstacle.”

Then he got his hands on GPT-4. The first thing he did was try those math problems. “The model nailed it,” he says. “Sitting here in 2024, of course GPT-4 can solve linear equations. But back then, this was crazy. GPT-3 cannot do that.”

But Bubeck’s real road-to-Damascus moment came when he pushed it to do something new.

The thing about middle school math problems is that they are all over the internet, and GPT-4 may simply have memorized them. “How do you study a model that may have seen everything that human beings have written?” asks Bubeck. His answer was to test GPT-4 on a range of problems that he and his colleagues believed to be novel.

Playing around with Ronen Eldan, a mathematician at Microsoft Research, Bubeck asked GPT-4 to give, in verse, a mathematical proof that there are an infinite number of primes.

Here’s a snippet of GPT-4’s response: “If we take the smallest number in S that is not in P / And call it p, we can add it to our set, don’t you see? / But this process can be repeated indefinitely. / Thus, our set P must also be infinite, you’ll agree.”

Cute, right? But Bubeck and Eldan thought it was much more than that. “We were in this office,” says Bubeck, waving at the room behind him via Zoom. “Both of us fell from our chairs. We couldn’t believe what we were seeing. It was just so creative and so, like, you know, different.” 

The Microsoft team also got GPT-4 to generate the code to add a horn to a cartoon picture of a unicorn drawn in Latex, a word processing program. Bubeck thinks this shows that the model could read the existing Latex code, understand what it depicted, and identify where the horn should go.

“There are many examples, but a few of them are smoking guns of reasoning,” he says—reasoning being a crucial building block of human intelligence.

Bubeck, Eldan, and a team of other Microsoft researchers described their findings in a paper that they called “ Spark s of artificial general intelligence ”: “We believe that GPT-4’s intelligence signals a true paradigm shift in the field of computer science and beyond.” When Bubeck shared the paper online, he tweeted : “time to face it, the sparks of #AGI have been ignited.”

The Sparks paper quickly became infamous—and a touchstone for AI boosters. Agüera y Arcas and Peter Norvig, a former director of research at Google and coauthor of Artificial Intelligence: A Modern Approach , perhaps the most popular AI textbook in the world, cowrote an article called “ Artificial General Intelligence Is Already Here .” Published in Noema , a magazine backed by an LA think tank called the Berggruen Institute, their argument uses the Sparks paper as a jumping-off point: “Artificial General Intelligence (AGI) means many different things to different people, but the most important parts of it have already been achieved by the current generation of advanced AI large language models,” they wrote. “Decades from now, they will be recognized as the first true examples of AGI.”

Since then, the hype has continued to balloon. Leopold Aschenbrenner, who at the time was a researcher at OpenAI focusing on superintelligence, told me last year: “AI progress in the last few years has been just extraordinarily rapid. We’ve been crushing all the benchmarks, and that progress is continuing unabated. But it won’t stop there. We’re going to have superhuman models, models that are much smarter than us.” (He was fired from OpenAI in April because, he claims, he raised security concerns about the tech he was building and “ ruffled some feathers .” He has since set up a Silicon Valley investment fund.)

In June, Aschenbrenner put out a 165-page manifesto arguing that AI will outpace college graduates by “2025/2026” and that “we will have superintelligence, in the true sense of the word” by the end of the decade. But others in the industry scoff at such claims. When Aschenbrenner tweeted a chart to show how fast he thought AI would continue to improve given how fast it had improved in last few years, the tech investor Christian Keil replied that by the same logic, his baby son, who had doubled in size since he was born, would weigh 7.5 trillion tons by the time he was 10.

It’s no surprise that “sparks of AGI” has also become a byword for over-the-top buzz. “I think they got carried away,” says Marcus, speaking about the Microsoft team. “They got excited, like ‘Hey, we found something! This is amazing!’ They didn’t vet it with the scientific community.” Bender refers to the Sparks paper as a “fan fiction novella.”

Not only was it provocative to claim that GPT-4’s behavior showed signs of AGI, but Microsoft, which uses GPT-4 in its own products, has a clear interest in promoting the capabilities of the technology. “This document is marketing fluff masquerading as research,” one tech COO posted on LinkedIn.

Some also felt the paper’s methodology was flawed. Its evidence is hard to verify because it comes from interactions with a version of GPT-4 that was not made available outside OpenAI and Microsoft. The public version has guardrails that restrict the model’s capabilities, admits Bubeck. This made it impossible for other researchers to re-create his experiments.

One group tried to re-create the unicorn example with a coding language called Processing, which GPT-4 can also use to generate images . They found that the public version of GPT-4 could produce a passable unicorn but not flip or rotate that image by 90 degrees. It may seem like a small difference, but such things really matter when you’re claiming that the ability to draw a unicorn is a sign of AGI.

The key thing about the examples in the Sparks paper, including the unicorn, is that Bubeck and his colleagues believe they are genuine examples of creative reasoning. This means the team had to be certain that examples of these tasks, or ones very like them, were not included anywhere in the vast data sets that OpenAI amassed to train its model. Otherwise, the results could be interpreted instead as instances where GPT-4 reproduced patterns it had already seen.

Bubeck insists that they set the model only tasks that would not be found on the internet. Drawing a cartoon unicorn in Latex was surely one such task. But the internet is a big place. Other researchers soon pointed out that there are indeed online forums dedicated to drawing animals in Latex . “Just fyi we knew about this,” Bubeck replied on X. “Every single query of the Sparks paper was thoroughly looked for on the internet.”

(This didn’t stop the name-calling: “I’m asking you to stop being a charlatan,” Ben Recht, a computer scientist at the University of California, Berkeley, tweeted back before accusing Bubeck of “being caught flat-out lying.”)

Bubeck insists the work was done in good faith, but he and his coauthors admit in the paper itself that their approach was not rigorous—notebook observations rather than foolproof experiments. 

Still, he has no regrets: “The paper has been out for more than a year and I have yet to see anyone give me a convincing argument that the unicorn, for example, is not a real example of reasoning.”

That’s not to say he can give me a straight answer to the big question—though his response reveals what kind of answer he’d like to give. “What is AI?” Bubeck repeats back to me. “I want to be clear with you. The question can be simple, but the answer can be complex.”

“There are many simple questions out there to which we still don’t know the answer. And some of those simple questions are the most profound ones,” he says. “I’m putting this on the same footing as, you know, What is the origin of life? What is the origin of the universe? Where did we come from? Big, big questions like this.”

Seeing only math in the machine

Before Bender became one of the chief antagonists of AI’s boosters, she made her mark on the AI world as a coauthor on two influential papers. (Both peer-reviewed, she likes to point out—unlike the Sparks paper and many of the others that get much of the attention.) The first, written with Alexander Koller, a fellow computational linguist at Saarland University in Germany, and published in 2020, was called “ Climbing towards NLU ” (NLU is natural-language understanding).

“The start of all this for me was arguing with other people in computational linguistics whether or not language models understand anything,” she says. (Understanding, like reasoning, is typically taken to be a basic ingredient of human intelligence.)

Bender and Koller argue that a model trained exclusively on text will only ever learn the form of a language, not its meaning. Meaning, they argue, consists of two parts: the words (which could be marks or sounds) plus the reason those words were uttered. People use language for many reasons, such as sharing information, telling jokes, flirting, warning somebody to back off, and so on. Stripped of that context, the text used to train LLMs like GPT-4 lets them mimic the patterns of language well enough for many sentences generated by the LLM to look exactly like sentences written by a human. But there’s no meaning behind them, no spark . It’s a remarkable statistical trick, but completely mindless.

They illustrate their point with a thought experiment. Imagine two English-speaking people stranded on neighboring deserted islands. There is an underwater cable that lets them send text messages to each other. Now imagine that an octopus, which knows nothing about English but is a whiz at statistical pattern matching, wraps its suckers around the cable and starts listening in to the messages. The octopus gets really good at guessing what words follow other words. So good that when it breaks the cable and starts replying to messages from one of the islanders, she believes that she is still chatting with her neighbor. (In case you missed it, the octopus in this story is a chatbot.)

The person talking to the octopus would stay fooled for a reasonable amount of time, but could that last? Does the octopus understand what comes down the wire? 

Imagine that the islander now says she has built a coconut catapult and asks the octopus to build one too and tell her what it thinks. The octopus cannot do this. Without knowing what the words in the messages refer to in the world, it cannot follow the islander’s instructions. Perhaps it guesses a reply: “Okay, cool idea!” The islander will probably take this to mean that the person she is speaking to understands her message. But if so, she is seeing meaning where there is none. Finally, imagine that the islander gets attacked by a bear and sends calls for help down the line. What is the octopus to do with these words?

Bender and Koller believe that this is how large language models learn and why they are limited. “The thought experiment shows why this path is not going to lead us to a machine that understands anything,” says Bender. “The deal with the octopus is that we have given it its training data, the conversations between those two people, and that’s it. But then here’s something that comes out of the blue and it won’t be able to deal with it because it hasn’t understood.”

The other paper Bender is known for, “ On the Dangers of Stochastic Parrots ,” highlights a series of harms that she and her coauthors believe the companies making large language models are ignoring. These include the huge computational costs of making the models and their environmental impact; the racist, sexist, and other abusive language the models entrench; and the dangers of building a system that could fool people by “haphazardly stitching together sequences of linguistic forms … according to probabilistic information about how they combine, but without any reference to meaning: a stochastic parrot.”

Google senior management wasn’t happy with the paper, and the resulting conflict led two of Bender’s coauthors, Timnit Gebru and Margaret Mitchell, to be forced out of the company, where they had led the AI Ethics team. It also made “stochastic parrot” a popular put-down for large language models—and landed Bender right in the middle of the name-calling merry-go-round.

The bottom line for Bender and for many like-minded researchers is that the field has been taken in by smoke and mirrors: “I think that they are led to imagine autonomous thinking entities that can make decisions for themselves and ultimately be the kind of thing that could actually be accountable for those decisions.”

Always the linguist, Bender is now at the point where she won’t even use the term AI “without scare quotes,” she tells me. Ultimately, for her, it’s a Big Tech buzzword that distracts from the many associated harms. “I’ve got skin in the game now,” she says. “I care about these issues, and the hype is getting in the way.”

Extraordinary evidence?

Agüera y Arcas calls people like Bender “AI denialists”—the implication being that they won’t ever accept what he takes for granted. Bender’s position is that extraordinary claims require extraordinary evidence, which we do not have.

But there are people looking for it, and until they find something clear-cut—sparks or stochastic parrots or something in between—they’d prefer to sit out the fight. Call this the wait-and-see camp.

As Ellie Pavlick, who studies neural networks at Brown University, tells me: “It’s offensive to some people to suggest that human intelligence could be re-created through these kinds of mechanisms.”

She adds, “People have strong-held beliefs about this issue—it almost feels religious. On the other hand, there’s people who have a little bit of a God complex. So it’s also offensive to them to suggest that they just can’t do it.”

Pavlick is ultimately agnostic. She’s a scientist, she insists, and will follow wherever the science leads. She rolls her eyes at the wilder claims, but she believes there’s something exciting going on. “That’s where I would disagree with Bender and Koller,” she tells me. “I think there’s actually some sparks—maybe not of AGI, but like, there’s some things in there that we didn’t expect to find.”

The problem is finding agreement on what those exciting things are and why they’re exciting. With so much hype, it’s easy to be cynical.

Researchers like Bubeck seem a lot more cool-headed when you hear them out. He thinks the infighting misses the nuance in his work. “I don’t see any problem in holding simultaneous views,” he says. “There is stochastic parroting; there is reasoning—it’s a spectrum. It’s very complex. We don’t have all the answers.”

“We need a completely new vocabulary to describe what’s going on,” he says. “One reason why people push back when I talk about reasoning in large language models is because it’s not the same reasoning as in human beings. But I think there is no way we can not call it reasoning. It is reasoning.”

Anthropic’s Olah plays it safe when pushed on what we’re seeing in LLMs, though his company, one of the hottest AI labs in the world right now, built Claude 3, an LLM that has received just as much hyperbolic praise as GPT-4 (if not more) since its release earlier this year.

“I feel like a lot of these conversations about the capabilities of these models are very tribal,” he says. “People have preexisting opinions, and it’s not very informed by evidence on any side. Then it just becomes kind of vibes-based, and I think vibes-based arguments on the internet tend to go in a bad direction.”

Olah tells me he has hunches of his own. “My subjective impression is that these things are tracking pretty sophisticated ideas,” he says. “We don’t have a comprehensive story of how very large models work, but I think it’s hard to reconcile what we’re seeing with the extreme ‘stochastic parrots’ picture.”

That’s as far as he’ll go: “I don’t want to go too much beyond what can be really strongly inferred from the evidence that we have.”

Last month, Anthropic released results from a study in which researchers gave Claude 3 the neural network equivalent of an MRI. By monitoring which bits of the model turned on and off as they ran it, they identified specific patterns of neurons that activated when the model was shown specific inputs.

Anthropic also reported patterns that it says correlate with inputs that attempt to describe or show abstract concepts. “We see features related to deception and honesty, to sycophancy, to security vulnerabilities, to bias,” says Olah. “We find features related to power seeking and manipulation and betrayal.”

ASK IT FOR A RECIPE pic.twitter.com/0ZM3uGRJi9 — heron @ SF (@iamaheron_) May 23, 2024

These results give one of the clearest looks yet at what’s inside a large language model. It’s a tantalizing glimpse at what look like elusive humanlike traits. But what does it really tell us? As Olah admits, they do not know what the model does with these patterns. “It’s a relatively limited picture, and the analysis is pretty hard,” he says.

Even if Olah won’t spell out exactly what he thinks goes on inside a large language model like Claude 3, it’s clear why the question matters to him. Anthropic is known for its work on AI safety—making sure that powerful future models will behave in ways we want them to and not in ways we don’t (known as “alignment” in industry jargon). Figuring out how today’s models work is not only a necessary first step if you want to control future ones; it also tells you how much you need to worry about doomer scenarios in the first place. “If you don’t think that models are going to be very capable,” says Olah, “then they’re probably not going to be very dangerous.”

Why we all can’t get along

In a 2014 interview with the BBC that looked back on her career, the influential cognitive scientist Margaret Boden, now 87, was asked if she thought there were any limits that would prevent computers (or “tin cans,” as she called them) from doing what humans can do.

“I certainly don’t think there’s anything in principle,” she said. “Because to deny that is to say that [human thinking] happens by magic, and I don’t believe that it happens by magic.”

But, she cautioned, powerful computers won’t be enough to get us there: the AI field will also need “powerful ideas”—new theories of how thinking happens, new algorithms that might reproduce it. “But these things are very, very difficult and I see no reason to assume that we will one of these days be able to answer all of those questions. Maybe we will; maybe we won’t.” 

Boden was reflecting on the early days of the current boom, but this will-we-or-won’t-we teetering speaks to decades in which she and her peers grappled with the same hard questions that researchers struggle with today. AI began as an ambitious aspiration 70-odd years ago and we are still disagreeing about what is and isn’t achievable, and how we’ll even know if we have achieved it. Most—if not all—of these disputes come down to this: We don’t have a good grasp on what intelligence is or how to recognize it. The field is full of hunches, but no one can say for sure.

We’ve been stuck on this point ever since people started taking the idea of AI seriously. Or even before that, when the stories we consumed started planting the idea of humanlike machines deep in our collective imagination. The long history of these disputes means that today’s fights often reinforce rifts that have been around since the beginning, making it even more difficult for people to find common ground.

To understand how we got here, we need to understand where we’ve been. So let’s dive into AI’s origin story—one that also played up the hype in a bid for cash.

A brief history of AI spin

The computer scientist John McCarthy is credited with coming up with the term “artificial intelligence” in 1955 when writing a funding application for a summer research program at Dartmouth College in New Hampshire.

The plan was for McCarthy and a small group of fellow researchers, a who’s-who of postwar US mathematicians and computer scientists—or “John McCarthy and the boys,” as Harry Law, a researcher who studies the history of AI at the University of Cambridge and ethics and policy at Google DeepMind, puts it—to get together for two months (not a typo) and make some serious headway on this new research challenge they’d set themselves.

“The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it,” McCarthy and his coauthors wrote. “An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”

That list of things they wanted to make machines do—what Bender calls “the starry-eyed dream”—hasn’t changed much. Using language, forming concepts, and solving problems are defining goals for AI today. The hubris hasn’t changed much either: “We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer,” they wrote. That summer, of course, has stretched to seven decades. And the extent to which these problems are in fact now solved is something that people still shout about on the internet. 

But what’s often left out of this canonical history is that artificial intelligence almost wasn’t called “artificial intelligence” at all.

More than one of McCarthy’s colleagues hated the term he had come up with. “The word ‘artificial’ makes you think there’s something kind of phony about this,” Arthur Samuel, a Dartmouth participant and creator of the first checkers-playing computer, is quoted as saying in historian Pamela McCorduck’s 2004 book Machines Who Think . The mathematician Claude Shannon, a coauthor of the Dartmouth proposal who is sometimes billed as “the father of the information age,” preferred the term “automata studies.” Herbert Simon and Allen Newell, two other AI pioneers, continued to call their own work “complex information processing” for years afterwards.

In fact, “artificial intelligence” was just one of several labels that might have captured the hodgepodge of ideas that the Dartmouth group was drawing on. The historian Jonnie Penn has identified possible alternatives that were in play at the time, including “engineering psychology,” “applied epistemology,” “neural cybernetics,” “non-numerical computing,” “neuraldynamics,” “advanced automatic programming,” and “hypothetical automata.” This list of names reveals how diverse the inspiration for their new field was, pulling from biology, neuroscience, statistics, and more. Marvin Minsky, another Dartmouth participant, has described AI as a “suitcase word” because it can hold so many divergent interpretations.

But McCarthy wanted a name that captured the ambitious scope of his vision. Calling this new field “artificial intelligence” grabbed people’s attention—and money. Don’t forget: AI is sexy, AI is cool.

In addition to terminology, the Dartmouth proposal codified a split between rival approaches to artificial intelligence that has divided the field ever since—a divide Law calls the “core tension in AI.”

McCarthy and his colleagues wanted to describe in computer code “every aspect of learning or any other feature of intelligence” so that machines could mimic them. In other words, if they could just figure out how thinking worked—the rules of reasoning—and write down the recipe, they could program computers to follow it. This laid the foundation of what came to be known as rule-based or symbolic AI (sometimes referred to now as GOFAI, “good old-fashioned AI”). But coming up with hard-coded rules that captured the processes of problem-solving for actual, nontrivial problems proved too hard.

The other path favored neural networks, computer programs that would try to learn those rules by themselves in the form of statistical patterns. The Dartmouth proposal mentions it almost as an aside (referring variously to “neuron nets” and “nerve nets”). Though the idea seemed less promising at first, some researchers nevertheless continued to work on versions of neural networks alongside symbolic AI. But it would take decades—plus vast amounts of computing power and much of the data on the internet—before they really took off. Fast-forward to today and this approach underpins the entire AI boom.

The big takeaway here is that, just like today’s researchers, AI’s innovators fought about foundational concepts and got caught up in their own promotional spin. Even team GOFAI was plagued by squabbles. Aaron Sloman, a philosopher and fellow AI pioneer now in his late 80s, recalls how “old friends” Minsky and McCarthy “disagreed strongly” when he got to know them in the ’70s: “Minsky thought McCarthy’s claims about logic could not work, and McCarthy thought Minsky’s mechanisms could not do what could be done using logic. I got on well with both of them, but I was saying, ‘Neither of you have got it right.’” (Sloman still thinks no one can account for the way human reasoning uses intuition as much as logic, but that’s yet another tangent!)

As the fortunes of the technology waxed and waned, the term “AI” went in and out of fashion. In the early ’70s, both research tracks were effectively put on ice after the UK government published a report arguing that the AI dream had gone nowhere and wasn’t worth funding. All that hype, effectively, had led to nothing. Research projects were shuttered, and computer scientists scrubbed the words “artificial intelligence” from their grant proposals.

When I was finishing a computer science PhD in 2008, only one person in the department was working on neural networks. Bender has a similar recollection: “When I was in college, a running joke was that AI is anything that we haven’t figured out how to do with computers yet. Like, as soon as you figure out how to do it, it wasn’t magic anymore, so it wasn’t AI.”

But that magic—the grand vision laid out in the Dartmouth proposal—remained alive and, as we can now see, laid the foundations for the AGI dream.

Good and bad behavior

In 1950, five years before McCarthy started talking about artificial intelligence, Alan Turing had published a paper that asked: Can machines think? To address that question, the famous mathematician proposed a hypothetical test, which he called the imitation game. The setup imagines a human and a computer behind a screen and a second human who types questions to each. If the questioner cannot tell which answers come from the human and which come from the computer, Turing claimed, the computer may as well be said to think.

What Turing saw—unlike McCarthy’s crew—was that thinking is a really difficult thing to describe. The Turing test was a way to sidestep that problem. “He basically said: Instead of focusing on the nature of intelligence itself, I’m going to look for its manifestation in the world. I’m going to look for its shadow ,” says Law.

In 1952, BBC Radio convened a panel to explore Turing’s ideas further. Turing was joined in the studio by two of his Manchester University colleagues—professor of mathematics Maxwell Newman and professor of neurosurgery Geoffrey Jefferson—and Richard Braithwaite, a philosopher of science, ethics, and religion at the University of Cambridge.

Braithwaite kicked things off: “Thinking is ordinarily regarded as so much the specialty of man, and perhaps of other higher animals, the question may seem too absurd to be discussed. But of course, it all depends on what is to be included in ‘thinking.’”

The panelists circled Turing’s question but never quite pinned it down.

When they tried to define what thinking involved, what its mechanisms were, the goalposts moved. “As soon as one can see the cause and effect working themselves out in the brain, one regards it as not being thinking but a sort of unimaginative donkey work,” said Turing.

Here was the problem: When one panelist proposed some behavior that might be taken as evidence of thought—reacting to a new idea with outrage, say—another would point out that a computer could be made to do it.

As Newman said, it would be easy enough to program a computer to print “I don’t like this new program.” But he admitted that this would be a trick.

Exactly, Jefferson said: He wanted a computer that would print “I don’t like this new program” because it didn’t like the new program. In other words, for Jefferson, behavior was not enough. It was the process leading to the behavior that mattered.

But Turing disagreed. As he had noted, uncovering a specific process—the donkey work, to use his phrase—did not pinpoint what thinking was either. So what was left?

“From this point of view, one might be tempted to define thinking as consisting of those mental processes that we don’t understand,” said Turing. “If this is right, then to make a thinking machine is to make one which does interesting things without our really understanding quite how it is done.”

It is strange to hear people grapple with these ideas for the first time. “The debate is prescient,” says Tomer Ullman, a cognitive scientist at Harvard University. “Some of the points are still alive—perhaps even more so. What they seem to be going round and round on is that the Turing test is first and foremost a behaviorist test.”

For Turing, intelligence was hard to define but easy to recognize. He proposed that the appearance of intelligence was enough—and said nothing about how that behavior should come about.

And yet most people, when pushed, will have a gut instinct about what is and isn’t intelligent. There are dumb ways and clever ways to come across as intelligent. In 1981, Ned Block, a philosopher at New York University, showed that Turing’s proposal fell short of those gut instincts. Because it said nothing of what caused the behavior, the Turing test can be beaten through trickery (as Newman had noted in the BBC broadcast).

“Could the issue of whether a machine in fact thinks or is intelligent depend on how gullible human interrogators tend to be?” asked Block. (Or as computer scientist Mark Reidl has remarked : “The Turing test is not for AI to pass but for humans to fail.”)

Imagine, Block said, a vast look-up table in which human programmers had entered all possible answers to all possible questions. Type a question into this machine, and it would look up a matching answer in its database and send it back. Block argued that anyone using this machine would judge its behavior to be intelligent: “But actually, the machine has the intelligence of a toaster,” he wrote. “All the intelligence it exhibits is that of its programmers.”

Block concluded that whether behavior is intelligent behavior is a matter of how it is produced, not how it appears. Block’s toasters, which became known as Blockheads, are one of the strongest counterexamples to the assumptions behind Turing’s proposal.

Looking under the hood

The Turing test is not meant to be a practical metric, but its implications are deeply ingrained in the way we think about artificial intelligence today. This has become particularly relevant as LLMs have exploded in the past several years. These models get ranked by their outward behaviors, specifically how well they do on a range of tests. When OpenAI announced GPT-4, it published an impressive-looking scorecard that detailed the model’s performance on multiple high school and professional exams. Almost nobody talks about how these models get those results.

That’s because we don’t know. Today’s large language models are too complex for anybody to say exactly how their behavior is produced. Researchers outside the small handful of companies making those models don’t know what’s in their training data; none of the model makers have shared details. That makes it hard to say what is and isn’t a kind of memorization—a stochastic parroting. But even researchers on the inside, like Olah, don’t know what’s really going on when faced with a bridge-obsessed bot.

This leaves the question wide open: Yes, large language models are built on math—but are they doing something intelligent with it?

And the arguments begin again.

“Most people are trying to armchair through it,” says Brown University’s Pavlick, meaning that they are arguing about theories without looking at what’s really happening. “Some people are like, ‘I think it’s this way,’ and some people are like, ‘Well, I don’t.’ We’re kind of stuck and everyone’s unsatisfied.”

Bender thinks that this sense of mystery plays into the mythmaking. (“Magicians do not explain their tricks,” she says.) Without a proper appreciation of where the LLM’s words come from, we fall back on familiar assumptions about humans, since that is our only real point of reference. When we talk to another person, we try to make sense of what that person is trying to tell us. “That process necessarily entails imagining a life behind the words,” says Bender. That’s how language works.

“The parlor trick of ChatGPT is so impressive that when we see these words coming out of it, we do the same thing instinctively,” she says. “It’s very good at mimicking the form of language. The problem is that we are not at all good at encountering the form of language and not imagining the rest of it.”

For some researchers, it doesn’t really matter if we can’t understand the how . Bubeck used to study large language models to try to figure out how they worked, but GPT-4 changed the way he thought about them. “It seems like these questions are not so relevant anymore,” he says. “The model is so big, so complex, that we can’t hope to open it up and understand what’s really happening.”

But Pavlick, like Olah, is trying to do just that. Her team has found that models seem to encode abstract relationships between objects, such as that between a country and its capital. Studying one large language model, Pavlick and her colleagues found that it used the same encoding to map France to Paris and Poland to Warsaw. That almost sounds smart, I tell her. “No, it’s literally a lookup table,” she says.

But what struck Pavlick was that, unlike a Blockhead, the model had learned this lookup table on its own. In other words, the LLM figured out itself that Paris is to France as Warsaw is to Poland. But what does this show? Is encoding its own lookup table instead of using a hard-coded one a sign of intelligence? Where do you draw the line?

“Basically, the problem is that behavior is the only thing we know how to measure reliably,” says Pavlick. “Anything else requires a theoretical commitment, and people don’t like having to make a theoretical commitment because it’s so loaded.”

Not all people. A lot of influential scientists are just fine with theoretical commitment. Hinton, for example, insists that neural networks are all you need to re-create humanlike intelligence. “Deep learning is going to be able to do everything,” he told MIT Technology Review in 2020 . 

It’s a commitment that Hinton seems to have held onto from the start. Sloman, who recalls the two of them arguing when Hinton was a graduate student in his lab, remembers being unable to persuade him that neural networks cannot learn certain crucial abstract concepts that humans and some other animals seem to have an intuitive grasp of, such as whether something is impossible. We can just see when something’s ruled out, Sloman says. “Despite Hinton’s outstanding intelligence, he never seemed to understand that point. I don’t know why, but there are large numbers of researchers in neural networks who share that failing.”

And then there’s Marcus, whose view of neural networks is the exact opposite of Hinton’s. His case draws on what he says scientists have discovered about brains.

Brains, Marcus points out, are not blank slates that learn fully from scratch—they come ready-made with innate structures and processes that guide learning. It’s how babies can learn things that the best neural networks still can’t, he argues.

“Neural network people have this hammer, and now everything is a nail,” says Marcus. “They want to do all of it with learning, which many cognitive scientists would find unrealistic and silly. You’re not going to learn everything from scratch.”

Not that Marcus—a cognitive scientist—is any less sure of himself. “If one really looked at who’s predicted the current situation well, I think I would have to be at the top of anybody’s list,” he tells me from the back of an Uber on his way to catch a flight to a speaking gig in Europe. “I know that doesn’t sound very modest, but I do have this perspective that turns out to be very important if what you’re trying to study is artificial intelligence.”

Given his well-publicized attacks on the field, it might surprise you that Marcus still believes AGI is on the horizon. It’s just that he thinks today’s fixation on neural networks is a mistake. “We probably need a breakthrough or two or four,” he says. “You and I might not live that long, I’m sorry to say. But I think it’ll happen this century. Maybe we’ve got a shot at it.”

The power of a technicolor dream

Over Dor Skuler’s shoulder on the Zoom call from his home in Ramat Gan, Israel, a little lamp-like robot is winking on and off while we talk about it. “You can see ElliQ behind me here,” he says. Skuler’s company, Intuition Robotics, develops these devices for older people, and the design—part Amazon Alexa, part R2-D2—must make it very clear that ElliQ is a computer. If any of his customers show signs of being confused about that, Intuition Robotics takes the device back, says Skuler.

ElliQ has no face, no humanlike shape at all. Ask it about sports, and it will crack a joke about having no hand-eye coordination because it has no hands and no eyes. “For the life of me, I don’t understand why the industry is trying to fulfill the Turing test,” Skuler says. “Why is it in the best interest of humanity for us to develop technology whose goal is to dupe us?”

Instead, Skuler’s firm is betting that people can form relationships with machines that present as machines. “Just like we have the ability to build a real relationship with a dog,” he says. “Dogs provide a lot of joy for people. They provide companionship. People love their dog—but they never confuse it to be a human.”

ElliQ’s users, many in their 80s and 90s, refer to the robot as an entity or a presence—sometimes a roommate. “They’re able to create a space for this in-between relationship, something between a device or a computer and something that’s alive,” says Skuler.

But no matter how hard ElliQ’s designers try to control the way people view the device, they are competing with decades of pop culture that have shaped our expectations. Why are we so fixated on AI that’s humanlike? “Because it’s hard for us to imagine something else,” says Skuler (who indeed refers to ElliQ as “she” throughout our conversation). “And because so many people in the tech industry are fans of science fiction. They try to make their dream come true.”

How many developers grew up today thinking that building a smart machine was seriously the coolest thing—if not the most important thing—that they could possibly do?

It was not long ago that OpenAI launched its new voice-controlled version of ChatGPT with a voice that sounded like Scarlett Johansson, after which many people—including Altman—flagged the connection to Spike Jonze’s 2013 movie Her .

Science fiction co-invents what AI is understood to be. As Cave and Dihal write in Imagining AI : “AI was a cultural phenomenon long before it was a technological one.”

Stories and myths about remaking humans as machines have been around for centuries. People have been dreaming of artificial humans for probably as long as they have dreamed of flight, says Dihal. She notes that Daedalus, the figure in Greek mythology famous for building a pair of wings for himself and his son, Icarus, also built what was effectively a giant bronze robot called Talos that threw rocks at passing pirates.

The word robot comes from robota , a term for “forced labor” coined by the Czech playwright Karel Čapek in his 1920 play Rossum’s Universal Robots . The “laws of robotics” outlined in Isaac Asimov’s science fiction, forbidding machines from harming humans, are inverted by movies like The Terminator , which is an iconic reference point for popular fears about real-world technology. The 2014 film Ex Machina is a dramatic riff on the Turing test. Last year’s blockbuster The Creator imagines a future world in which AI has been outlawed because it set off a nuclear bomb, an event that some doomers consider at least an outside possibility.

Cave and Dihal relate how another movie, 2014’s Transcendence , in which an AI expert played by Johnny Depp gets his mind uploaded to a computer, served a narrative pushed by ur-doomers Stephen Hawking, fellow physicist Max Tegmark, and AI researcher Stuart Russell. In an article published in the Huffington Post on the movie’s opening weekend, the trio wrote: “As the Hollywood blockbuster Transcendence debuts this weekend with … clashing visions for the future of humanity, it’s tempting to dismiss the notion of highly intelligent machines as mere science fiction. But this would be a mistake, and potentially our worst mistake ever.”

Right around the same time, Tegmark founded the Future of Life Institute, with a remit to study and promote AI safety. Depp’s costar in the movie, Morgan Freeman, was on the institute’s board, and Elon Musk, who had a cameo in the film, donated $10 million in its first year. For Cave and Dihal, Transcendence is a perfect example of the multiple entanglements between popular culture, academic research, industrial production, and “the billionaire-funded fight to shape the future.”

On the London leg of his world tour last year, Altman was asked what he’d meant when he tweeted : “AI is the tech the world has always wanted.” Standing at the back of the room that day, behind an audience of hundreds, I listened to him offer his own kind of origin story: “I was, like, a very nervous kid. I read a lot of sci-fi. I spent a lot of Friday nights home, playing on the computer. But I was always really interested in AI and I thought it’d be very cool.” He went to college, got rich, and watched as neural networks became better and better. “This can be tremendously good but also could be really bad. What are we going to do about that?” he recalled thinking in 2015. “I ended up starting OpenAI.”

Why you should care that a bunch of nerds are fighting about AI

Okay, you get it: No one can agree on what AI is. But what everyone does seem to agree on is that the current debate around AI has moved far beyond the academic and the scientific. There are political and moral components in play—which doesn’t help with everyone thinking everyone else is wrong.

Untangling this is hard. It can be difficult to see what’s going on when some of those moral views take in the entire future of humanity and anchor them in a technology that nobody can quite define.

But we can't just throw our hands up and walk away. Because no matter what this technology is, it’s coming, and unless you live under a rock, you’ll use it in one form or another. And the form that technology takes—and the problems it both solves and creates—will be shaped by the thinking and the motivations of people like the ones you just read about. In particular, by the people with the most power, the most cash, and the biggest megaphones.

Which leads me to the TESCREALists. Wait, come back! I realize it’s unfair to introduce yet another new concept so late in the game. But to understand how the people in power may mold the technologies they build, and how they explain them to the world’s regulators and lawmakers, you need to really understand their mindset.

Gebru, who founded the Distributed AI Research Institute after leaving Google, and Émile Torres, a philosopher and historian at Case Western Reserve University, have traced the influence of several techno-utopian belief systems on Silicon Valley. The pair argue that to understand what’s going on with AI right now—both why companies such as Google DeepMind and OpenAI are in a race to build AGI and why doomers like Tegmark and Hinton warn of a coming catastrophe—the field must be seen through the lens of what Torres has dubbed the TESCREAL framework .

The clunky acronym (pronounced tes-cree-all ) replaces an even clunkier list of labels: transhumanism , extropianism , singularitarianism , cosmism , rationalism , effective altruism , and longtermism . A lot has been written (and will be written) about each of these worldviews, so I’ll spare you here. (There are rabbit holes within rabbit holes for anyone wanting to dive deeper. Pick your forum and pack your spelunking gear.)

This constellation of overlapping ideologies is attractive to a certain kind of galaxy-brain mindset common in the Western tech world. Some anticipate human immortality; others predict humanity’s colonization of the stars. The common tenet is that an all-powerful technology—AGI or superintelligence, choose your team—is not only within reach but inevitable. You can see this in the do-or-die attitude that’s ubiquitous inside cutting-edge labs like OpenAI: If we don’t make AGI, someone else will.

What’s more, TESCREALists believe that AGI could not only fix the world’s problems but level up humanity. “The development and proliferation of AI—far from a risk that we should fear—is a moral obligation that we have to ourselves, to our children and to our future,” Andreessen wrote in a much-dissected manifesto last year. I have been told many times over that AGI is the way to make the world a better place—by Demis Hassabis , CEO and cofounder of Google DeepMind; by Mustafa Suleyman , CEO of the newly minted Microsoft AI and another cofounder of DeepMind; by Sutskever , Altman , and more.

But as Andreessen noted, it’s a yin-yang mindset. The flip side of techno-utopia is techno-hell. If you believe that you are building a technology so powerful that it will solve all the world’s problems, you probably also believe there’s a non-zero chance it will all go very wrong. When asked at the World Government Summit in February what keeps him up at night, Altman replied: “It’s all the sci-fi stuff.”

It’s a tension that Hinton has been talking up for the last year. It’s what companies like Anthropic claim to address. It’s what Sutskever is focusing on in his new lab , and what he wanted a special in-house team at OpenAI to focus on last year before disagreements over the way the company balanced risk and reward led most members of that team to leave.

Sure, doomerism is part of the spin. (“Claiming that you have created something that is super-intelligent is good for sales figures,” says Dihal. “It’s like, ‘Please, someone stop me from being so good and so powerful.’”) But boom or doom, exactly what (and whose) problems are these guys supposedly solving? Are we really expected to trust what they build and what they tell our leaders?

Gebru and Torres (and others) are adamant: No, we should not. They are highly critical of these ideologies and how they may influence the development of future technology, especially AI. Fundamentally, they link several of these worldviews—with their common focus on “improving” humanity—to the racist eugenics movements of the 20th century.

One danger, they argue, is that a shift of resources toward the kind of technological innovations that these ideologies demand, from building AGI to extending life spans to colonizing other planets, will ultimately benefit people who are Western and white at the cost of billions of people who aren’t. If your sight is set on fantastical futures, it’s easy to overlook the present-day costs of innovation, such as labor exploitation, the entrenchment of racist and sexist bias, and environmental damage.  

“Are we trying to build a tool that’s useful to us in some way?” asks Bender, reflecting on the casualties of this race to AGI. If so, who’s it for, how do we test it, how well does it work? “But if what we’re building it for is just so that we can say that we’ve done it, that’s not a goal that I can get behind. That’s not a goal that’s worth billions of dollars.”

Bender says that seeing the connections between the TESCREAL ideologies is what made her realize there was something more to these debates. “Tangling with those people was—” she stops. “Okay, there’s more here than just academic ideas. There’s a moral code tied up in it as well.”

Of course, laid out like this without nuance, it doesn’t sound as if we—as a society, as individuals—are getting the best deal. It also all sounds rather silly. When Gebru described parts of the TESCREAL bundle in a talk last year, her audience laughed. It’s also true that few people would identify themselves as card-carrying students of these schools of thought, at least in their extremes.

But if we don’t understand how those building this tech approach it, how can we decide what deals we want to make? What apps we decide to use, what chatbots we want to give personal information to, what data centers we support in our neighborhoods, what politicians we want to vote for?

It used to be like this: There was a problem in the world, and we built something to fix it. Here, everything is backward: The goal seems to be to build a machine that can do everything, and to skip the slow, hard work that goes into figuring out what the problem is before building the solution.

And as Gebru said in that same talk, “A machine that solves all problems: if that’s not magic, what is it?”

Semantics, semantics … semantics?

When asked outright what AI is, a lot of people dodge the question. Not Suleyman. In April, the CEO of Microsoft AI stood on the TED stage and told the audience what he’d told his six-year-old nephew in response to that question. The best answer he could give, Suleyman explained, was that AI was “a new kind of digital species”—a technology so universal, so powerful, that calling it a tool no longer captured what it could do for us.

“On our current trajectory, we are heading toward the emergence of something we are all struggling to describe, and yet we cannot control what we don’t understand,” he said. “And so the metaphors, the mental models, the names—these all matter if we are to get the most out of AI whilst limiting its potential downsides.”

Language matters! I hope that’s clear from the twists and turns and tantrums we’ve been through to get to this point. But I also hope you’re asking: Whose language? And whose downsides? Suleyman is an industry leader at a technology giant that stands to make billions from its AI products. Describing the technology behind those products as a new kind of species conjures something wholly unprecedented, something with agency and capabilities that we have never seen before. That makes my spidey sense tingle. You?

I can’t tell you if there’s magic here (ironically or not). And I can’t tell you how math can realize what Bubeck and many others see in this technology (no one can yet). You’ll have to make up your own mind. But I can pull back the curtain on my own point of view.

Writing about GPT-3 back in 2020, I said that the greatest trick AI ever pulled was convincing the world it exists. I still think that: We are hardwired to see intelligence in things that behave in certain ways, whether it’s there or not. In the last few years, the tech industry has found reasons of its own to convince us that AI exists, too. This makes me skeptical of many of the claims made for this technology.

With large language models—via their smiley-face masks—we are confronted by something we’ve never had to think about before. “It’s taking this hypothetical thing and making it really concrete,” says Pavlick. “I’ve never had to think about whether a piece of language required intelligence to generate because I’ve just never dealt with language that didn’t.”

AI is many things. But I don’t think it’s humanlike. I don’t think it’s the solution to all (or even most) of our problems. It isn’t ChatGPT or Gemini or Copilot. It isn’t neural networks. It’s an idea, a vision, a kind of wish fulfillment. And ideas get shaped by other ideas, by morals, by quasi-religious convictions, by worldviews, by politics, and by gut instinct. “Artificial intelligence” is a helpful shorthand to describe a raft of different technologies. But AI is not one thing; it never has been, no matter how often the branding gets seared into the outside of the box. 

“The truth is these words”—intelligence, reasoning, understanding, and more—“were defined before there was a need to be really precise about it,” says Pavlick. “I don’t really like when the question becomes ‘Does the model understand—yes or no?’ because, well, I don’t know. Words get redefined and concepts evolve all the time.”

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Topic for MIT "for fun" essay

Hi! I had a question about this MIT essay:

We know you lead a busy life, full of activities, many of which are required of you. Tell us about something you do simply for the pleasure of it.

I was wondering if I could write about something I used to do but can't do anymore? Specifically its about a certain exhibition i used to go to like every weekend for half a year before it closed