how to write a thesis draft

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Tips and Examples for Writing Thesis Statements

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This resource provides tips for creating a thesis statement and examples of different types of thesis statements.

Tips for Writing Your Thesis Statement

1. Determine what kind of paper you are writing:

• An analytical paper breaks down an issue or an idea into its component parts, evaluates the issue or idea, and presents this breakdown and evaluation to the audience.
• An expository (explanatory) paper explains something to the audience.
• An argumentative paper makes a claim about a topic and justifies this claim with specific evidence. The claim could be an opinion, a policy proposal, an evaluation, a cause-and-effect statement, or an interpretation. The goal of the argumentative paper is to convince the audience that the claim is true based on the evidence provided.

If you are writing a text that does not fall under these three categories (e.g., a narrative), a thesis statement somewhere in the first paragraph could still be helpful to your reader.

2. Your thesis statement should be specific—it should cover only what you will discuss in your paper and should be supported with specific evidence.

3. The thesis statement usually appears at the end of the first paragraph of a paper.

4. Your topic may change as you write, so you may need to revise your thesis statement to reflect exactly what you have discussed in the paper.

Thesis Statement Examples

Example of an analytical thesis statement:

The paper that follows should:

• Explain the analysis of the college admission process
• Explain the challenge facing admissions counselors

Example of an expository (explanatory) thesis statement:

• Explain how students spend their time studying, attending class, and socializing with peers

Example of an argumentative thesis statement:

• Present an argument and give evidence to support the claim that students should pursue community projects before entering college

While Sandel argues that pursuing perfection through genetic engineering would decrease our sense of humility, he claims that the sense of solidarity we would lose is also important.

This thesis summarizes several points in Sandel’s argument, but it does not make a claim about how we should understand his argument. A reader who read Sandel’s argument would not also need to read an essay based on this descriptive thesis.  

Broad thesis (arguable, but difficult to support with evidence) 

Michael Sandel’s arguments about genetic engineering do not take into consideration all the relevant issues.

This is an arguable claim because it would be possible to argue against it by saying that Michael Sandel’s arguments do take all of the relevant issues into consideration. But the claim is too broad. Because the thesis does not specify which “issues” it is focused on—or why it matters if they are considered—readers won’t know what the rest of the essay will argue, and the writer won’t know what to focus on. If there is a particular issue that Sandel does not address, then a more specific version of the thesis would include that issue—hand an explanation of why it is important.  

Arguable thesis with analytical claim 

While Sandel argues persuasively that our instinct to “remake” (54) ourselves into something ever more perfect is a problem, his belief that we can always draw a line between what is medically necessary and what makes us simply “better than well” (51) is less convincing.

This is an arguable analytical claim. To argue for this claim, the essay writer will need to show how evidence from the article itself points to this interpretation. It’s also a reasonable scope for a thesis because it can be supported with evidence available in the text and is neither too broad nor too narrow.  

Arguable thesis with normative claim 

Given Sandel’s argument against genetic enhancement, we should not allow parents to decide on using Human Growth Hormone for their children.

This thesis tells us what we should do about a particular issue discussed in Sandel’s article, but it does not tell us how we should understand Sandel’s argument.  

Questions to ask about your thesis 

• Is the thesis truly arguable? Does it speak to a genuine dilemma in the source, or would most readers automatically agree with it?  
• Is the thesis too obvious? Again, would most or all readers agree with it without needing to see your argument?  
• Is the thesis complex enough to require a whole essay's worth of argument?  
• Is the thesis supportable with evidence from the text rather than with generalizations or outside research?  
• Would anyone want to read a paper in which this thesis was developed? That is, can you explain what this paper is adding to our understanding of a problem, question, or topic?
• picture_as_pdf Thesis

Reference management. Clean and simple.

How to write a thesis statement + examples

What is a thesis statement?

Is a thesis statement a question, how do you write a good thesis statement, how do i know if my thesis statement is good, examples of thesis statements, helpful resources on how to write a thesis statement, frequently asked questions about writing a thesis statement, related articles.

A thesis statement is the main argument of your paper or thesis.

The thesis statement is one of the most important elements of any piece of academic writing . It is a brief statement of your paper’s main argument. Essentially, you are stating what you will be writing about.

You can see your thesis statement as an answer to a question. While it also contains the question, it should really give an answer to the question with new information and not just restate or reiterate it.

Your thesis statement is part of your introduction. Learn more about how to write a good thesis introduction in our introduction guide .

A thesis statement is not a question. A statement must be arguable and provable through evidence and analysis. While your thesis might stem from a research question, it should be in the form of a statement.

Tip: A thesis statement is typically 1-2 sentences. For a longer project like a thesis, the statement may be several sentences or a paragraph.

A good thesis statement needs to do the following:

• Condense the main idea of your thesis into one or two sentences.
• Answer your project’s main research question.
• Clearly state your position in relation to the topic .
• Make an argument that requires support or evidence.

Once you have written down a thesis statement, check if it fulfills the following criteria:

• Your statement needs to be provable by evidence. As an argument, a thesis statement needs to be debatable.
• Your statement needs to be precise. Do not give away too much information in the thesis statement and do not load it with unnecessary information.
• Your statement cannot say that one solution is simply right or simply wrong as a matter of fact. You should draw upon verified facts to persuade the reader of your solution, but you cannot just declare something as right or wrong.

As previously mentioned, your thesis statement should answer a question.

If the question is:

What do you think the City of New York should do to reduce traffic congestion?

A good thesis statement restates the question and answers it:

In this paper, I will argue that the City of New York should focus on providing exclusive lanes for public transport and adaptive traffic signals to reduce traffic congestion by the year 2035.

Here is another example. If the question is:

How can we end poverty?

A good thesis statement should give more than one solution to the problem in question:

In this paper, I will argue that introducing universal basic income can help reduce poverty and positively impact the way we work.

• The Writing Center of the University of North Carolina has a list of questions to ask to see if your thesis is strong .

A thesis statement is part of the introduction of your paper. It is usually found in the first or second paragraph to let the reader know your research purpose from the beginning.

In general, a thesis statement should have one or two sentences. But the length really depends on the overall length of your project. Take a look at our guide about the length of thesis statements for more insight on this topic.

Here is a list of Thesis Statement Examples that will help you understand better how to write them.

Every good essay should include a thesis statement as part of its introduction, no matter the academic level. Of course, if you are a high school student you are not expected to have the same type of thesis as a PhD student.

Here is a great YouTube tutorial showing How To Write An Essay: Thesis Statements .

Developing a Thesis Statement

Many papers you write require developing a thesis statement. In this section you’ll learn what a thesis statement is and how to write one.

Keep in mind that not all papers require thesis statements . If in doubt, please consult your instructor for assistance.

What is a thesis statement?

A thesis statement . . .

• Makes an argumentative assertion about a topic; it states the conclusions that you have reached about your topic.
• Makes a promise to the reader about the scope, purpose, and direction of your paper.
• Is focused and specific enough to be “proven” within the boundaries of your paper.
• Is generally located near the end of the introduction ; sometimes, in a long paper, the thesis will be expressed in several sentences or in an entire paragraph.
• Identifies the relationships between the pieces of evidence that you are using to support your argument.

Not all papers require thesis statements! Ask your instructor if you’re in doubt whether you need one.

Identify a topic

Your topic is the subject about which you will write. Your assignment may suggest several ways of looking at a topic; or it may name a fairly general concept that you will explore or analyze in your paper.

Consider what your assignment asks you to do

Inform yourself about your topic, focus on one aspect of your topic, ask yourself whether your topic is worthy of your efforts, generate a topic from an assignment.

Below are some possible topics based on sample assignments.

Sample assignment 1

Analyze Spain’s neutrality in World War II.

Identified topic

Franco’s role in the diplomatic relationships between the Allies and the Axis

This topic avoids generalities such as “Spain” and “World War II,” addressing instead on Franco’s role (a specific aspect of “Spain”) and the diplomatic relations between the Allies and Axis (a specific aspect of World War II).

Sample assignment 2

Analyze one of Homer’s epic similes in the Iliad.

The relationship between the portrayal of warfare and the epic simile about Simoisius at 4.547-64.

This topic focuses on a single simile and relates it to a single aspect of the Iliad ( warfare being a major theme in that work).

Developing a Thesis Statement–Additional information

Your assignment may suggest several ways of looking at a topic, or it may name a fairly general concept that you will explore or analyze in your paper. You’ll want to read your assignment carefully, looking for key terms that you can use to focus your topic.

Sample assignment: Analyze Spain’s neutrality in World War II Key terms: analyze, Spain’s neutrality, World War II

After you’ve identified the key words in your topic, the next step is to read about them in several sources, or generate as much information as possible through an analysis of your topic. Obviously, the more material or knowledge you have, the more possibilities will be available for a strong argument. For the sample assignment above, you’ll want to look at books and articles on World War II in general, and Spain’s neutrality in particular.

As you consider your options, you must decide to focus on one aspect of your topic. This means that you cannot include everything you’ve learned about your topic, nor should you go off in several directions. If you end up covering too many different aspects of a topic, your paper will sprawl and be unconvincing in its argument, and it most likely will not fulfull the assignment requirements.

For the sample assignment above, both Spain’s neutrality and World War II are topics far too broad to explore in a paper. You may instead decide to focus on Franco’s role in the diplomatic relationships between the Allies and the Axis , which narrows down what aspects of Spain’s neutrality and World War II you want to discuss, as well as establishes a specific link between those two aspects.

Before you go too far, however, ask yourself whether your topic is worthy of your efforts. Try to avoid topics that already have too much written about them (i.e., “eating disorders and body image among adolescent women”) or that simply are not important (i.e. “why I like ice cream”). These topics may lead to a thesis that is either dry fact or a weird claim that cannot be supported. A good thesis falls somewhere between the two extremes. To arrive at this point, ask yourself what is new, interesting, contestable, or controversial about your topic.

As you work on your thesis, remember to keep the rest of your paper in mind at all times . Sometimes your thesis needs to evolve as you develop new insights, find new evidence, or take a different approach to your topic.

Derive a main point from topic

Once you have a topic, you will have to decide what the main point of your paper will be. This point, the “controlling idea,” becomes the core of your argument (thesis statement) and it is the unifying idea to which you will relate all your sub-theses. You can then turn this “controlling idea” into a purpose statement about what you intend to do in your paper.

Look for patterns in your evidence

Compose a purpose statement.

Consult the examples below for suggestions on how to look for patterns in your evidence and construct a purpose statement.

• Franco first tried to negotiate with the Axis
• Franco turned to the Allies when he couldn’t get some concessions that he wanted from the Axis

Possible conclusion:

Spain’s neutrality in WWII occurred for an entirely personal reason: Franco’s desire to preserve his own (and Spain’s) power.

Purpose statement

This paper will analyze Franco’s diplomacy during World War II to see how it contributed to Spain’s neutrality.

• The simile compares Simoisius to a tree, which is a peaceful, natural image.
• The tree in the simile is chopped down to make wheels for a chariot, which is an object used in warfare.

At first, the simile seems to take the reader away from the world of warfare, but we end up back in that world by the end.

This paper will analyze the way the simile about Simoisius at 4.547-64 moves in and out of the world of warfare.

Derive purpose statement from topic

To find out what your “controlling idea” is, you have to examine and evaluate your evidence . As you consider your evidence, you may notice patterns emerging, data repeated in more than one source, or facts that favor one view more than another. These patterns or data may then lead you to some conclusions about your topic and suggest that you can successfully argue for one idea better than another.

For instance, you might find out that Franco first tried to negotiate with the Axis, but when he couldn’t get some concessions that he wanted from them, he turned to the Allies. As you read more about Franco’s decisions, you may conclude that Spain’s neutrality in WWII occurred for an entirely personal reason: his desire to preserve his own (and Spain’s) power. Based on this conclusion, you can then write a trial thesis statement to help you decide what material belongs in your paper.

Sometimes you won’t be able to find a focus or identify your “spin” or specific argument immediately. Like some writers, you might begin with a purpose statement just to get yourself going. A purpose statement is one or more sentences that announce your topic and indicate the structure of the paper but do not state the conclusions you have drawn . Thus, you might begin with something like this:

• This paper will look at modern language to see if it reflects male dominance or female oppression.
• I plan to analyze anger and derision in offensive language to see if they represent a challenge of society’s authority.

At some point, you can turn a purpose statement into a thesis statement. As you think and write about your topic, you can restrict, clarify, and refine your argument, crafting your thesis statement to reflect your thinking.

As you work on your thesis, remember to keep the rest of your paper in mind at all times. Sometimes your thesis needs to evolve as you develop new insights, find new evidence, or take a different approach to your topic.

Compose a draft thesis statement

If you are writing a paper that will have an argumentative thesis and are having trouble getting started, the techniques in the table below may help you develop a temporary or “working” thesis statement.

Begin with a purpose statement that you will later turn into a thesis statement.

Assignment: Discuss the history of the Reform Party and explain its influence on the 1990 presidential and Congressional election.

Purpose Statement: This paper briefly sketches the history of the grassroots, conservative, Perot-led Reform Party and analyzes how it influenced the economic and social ideologies of the two mainstream parties.

Question-to-Assertion

If your assignment asks a specific question(s), turn the question(s) into an assertion and give reasons why it is true or reasons for your opinion.

Assignment : What do Aylmer and Rappaccini have to be proud of? Why aren’t they satisfied with these things? How does pride, as demonstrated in “The Birthmark” and “Rappaccini’s Daughter,” lead to unexpected problems?

Beginning thesis statement: Alymer and Rappaccinni are proud of their great knowledge; however, they are also very greedy and are driven to use their knowledge to alter some aspect of nature as a test of their ability. Evil results when they try to “play God.”

Write a sentence that summarizes the main idea of the essay you plan to write.

Main idea: The reason some toys succeed in the market is that they appeal to the consumers’ sense of the ridiculous and their basic desire to laugh at themselves.

Make a list of the ideas that you want to include; consider the ideas and try to group them.

• nature = peaceful
• war matériel = violent (competes with 1?)
• need for time and space to mourn the dead
• war is inescapable (competes with 3?)

Use a formula to arrive at a working thesis statement (you will revise this later).

• although most readers of _______ have argued that _______, closer examination shows that _______.
• _______ uses _______ and _____ to prove that ________.
• phenomenon x is a result of the combination of __________, __________, and _________.

What to keep in mind as you draft an initial thesis statement

Beginning statements obtained through the methods illustrated above can serve as a framework for planning or drafting your paper, but remember they’re not yet the specific, argumentative thesis you want for the final version of your paper. In fact, in its first stages, a thesis statement usually is ill-formed or rough and serves only as a planning tool.

As you write, you may discover evidence that does not fit your temporary or “working” thesis. Or you may reach deeper insights about your topic as you do more research, and you will find that your thesis statement has to be more complicated to match the evidence that you want to use.

You must be willing to reject or omit some evidence in order to keep your paper cohesive and your reader focused. Or you may have to revise your thesis to match the evidence and insights that you want to discuss. Read your draft carefully, noting the conclusions you have drawn and the major ideas which support or prove those conclusions. These will be the elements of your final thesis statement.

Sometimes you will not be able to identify these elements in your early drafts, but as you consider how your argument is developing and how your evidence supports your main idea, ask yourself, “ What is the main point that I want to prove/discuss? ” and “ How will I convince the reader that this is true? ” When you can answer these questions, then you can begin to refine the thesis statement.

Refine and polish the thesis statement

To get to your final thesis, you’ll need to refine your draft thesis so that it’s specific and arguable.

• Ask if your draft thesis addresses the assignment
• Question each part of your draft thesis
• Clarify vague phrases and assertions
• Investigate alternatives to your draft thesis

Consult the example below for suggestions on how to refine your draft thesis statement.

Sample Assignment

Choose an activity and define it as a symbol of American culture. Your essay should cause the reader to think critically about the society which produces and enjoys that activity.

• Ask The phenomenon of drive-in facilities is an interesting symbol of american culture, and these facilities demonstrate significant characteristics of our society.This statement does not fulfill the assignment because it does not require the reader to think critically about society.

Drive-ins are an interesting symbol of American culture because they represent Americans’ significant creativity and business ingenuity.

Among the types of drive-in facilities familiar during the twentieth century, drive-in movie theaters best represent American creativity, not merely because they were the forerunner of later drive-ins and drive-throughs, but because of their impact on our culture: they changed our relationship to the automobile, changed the way people experienced movies, and changed movie-going into a family activity.

While drive-in facilities such as those at fast-food establishments, banks, pharmacies, and dry cleaners symbolize America’s economic ingenuity, they also have affected our personal standards.

While drive-in facilities such as those at fast- food restaurants, banks, pharmacies, and dry cleaners symbolize (1) Americans’ business ingenuity, they also have contributed (2) to an increasing homogenization of our culture, (3) a willingness to depersonalize relationships with others, and (4) a tendency to sacrifice quality for convenience.

This statement is now specific and fulfills all parts of the assignment. This version, like any good thesis, is not self-evident; its points, 1-4, will have to be proven with evidence in the body of the paper. The numbers in this statement indicate the order in which the points will be presented. Depending on the length of the paper, there could be one paragraph for each numbered item or there could be blocks of paragraph for even pages for each one.

Complete the final thesis statement

The bottom line.

As you move through the process of crafting a thesis, you’ll need to remember four things:

• Context matters! Think about your course materials and lectures. Try to relate your thesis to the ideas your instructor is discussing.
• As you go through the process described in this section, always keep your assignment in mind . You will be more successful when your thesis (and paper) responds to the assignment than if it argues a semi-related idea.
• Your thesis statement should be precise, focused, and contestable ; it should predict the sub-theses or blocks of information that you will use to prove your argument.
• Make sure that you keep the rest of your paper in mind at all times. Change your thesis as your paper evolves, because you do not want your thesis to promise more than your paper actually delivers.

In the beginning, the thesis statement was a tool to help you sharpen your focus, limit material and establish the paper’s purpose. When your paper is finished, however, the thesis statement becomes a tool for your reader. It tells the reader what you have learned about your topic and what evidence led you to your conclusion. It keeps the reader on track–well able to understand and appreciate your argument.

Writing Process and Structure

This is an accordion element with a series of buttons that open and close related content panels.

Getting Started with Your Paper

Interpreting Writing Assignments from Your Courses

Generating Ideas for

Creating an Argument

Thesis vs. Purpose Statements

Architecture of Arguments

Working with Sources

Quoting and Paraphrasing Sources

Using Literary Quotations

Citing Sources in Your Paper

Drafting Your Paper

Generating Ideas for Your Paper

Introductions

Paragraphing

Developing Strategic Transitions

Conclusions

Revising Your Paper

Peer Reviews

Reverse Outlines

Revising an Argumentative Paper

Revision Strategies for Longer Projects

Finishing Your Paper

Twelve Common Errors: An Editing Checklist

How to Proofread your Paper

Writing Collaboratively

Collaborative and Group Writing

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How to write a thesis statement, what is a thesis statement.

Almost all of us—even if we don’t do it consciously—look early in an essay for a one- or two-sentence condensation of the argument or analysis that is to follow. We refer to that condensation as a thesis statement.

Why Should Your Essay Contain a Thesis Statement?

• to test your ideas by distilling them into a sentence or two
• to better organize and develop your argument
• to provide your reader with a “guide” to your argument

In general, your thesis statement will accomplish these goals if you think of the thesis as the answer to the question your paper explores.

How Can You Write a Good Thesis Statement?

Here are some helpful hints to get you started. You can either scroll down or select a link to a specific topic.

How to Generate a Thesis Statement if the Topic is Assigned How to Generate a Thesis Statement if the Topic is not Assigned How to Tell a Strong Thesis Statement from a Weak One

How to Generate a Thesis Statement if the Topic is Assigned

Almost all assignments, no matter how complicated, can be reduced to a single question. Your first step, then, is to distill the assignment into a specific question. For example, if your assignment is, “Write a report to the local school board explaining the potential benefits of using computers in a fourth-grade class,” turn the request into a question like, “What are the potential benefits of using computers in a fourth-grade class?” After you’ve chosen the question your essay will answer, compose one or two complete sentences answering that question.

Q: “What are the potential benefits of using computers in a fourth-grade class?” A: “The potential benefits of using computers in a fourth-grade class are . . .”

A: “Using computers in a fourth-grade class promises to improve . . .”

The answer to the question is the thesis statement for the essay.

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How to Generate a Thesis Statement if the Topic is not Assigned

Even if your assignment doesn’t ask a specific question, your thesis statement still needs to answer a question about the issue you’d like to explore. In this situation, your job is to figure out what question you’d like to write about.

A good thesis statement will usually include the following four attributes:

• take on a subject upon which reasonable people could disagree
• deal with a subject that can be adequately treated given the nature of the assignment
• express one main idea
• assert your conclusions about a subject

Let’s see how to generate a thesis statement for a social policy paper.

Brainstorm the topic . Let’s say that your class focuses upon the problems posed by changes in the dietary habits of Americans. You find that you are interested in the amount of sugar Americans consume.

You start out with a thesis statement like this:

Sugar consumption.

This fragment isn’t a thesis statement. Instead, it simply indicates a general subject. Furthermore, your reader doesn’t know what you want to say about sugar consumption.

Narrow the topic . Your readings about the topic, however, have led you to the conclusion that elementary school children are consuming far more sugar than is healthy.

You change your thesis to look like this:

Reducing sugar consumption by elementary school children.

This fragment not only announces your subject, but it focuses on one segment of the population: elementary school children. Furthermore, it raises a subject upon which reasonable people could disagree, because while most people might agree that children consume more sugar than they used to, not everyone would agree on what should be done or who should do it. You should note that this fragment is not a thesis statement because your reader doesn’t know your conclusions on the topic.

Take a position on the topic. After reflecting on the topic a little while longer, you decide that what you really want to say about this topic is that something should be done to reduce the amount of sugar these children consume.

You revise your thesis statement to look like this:

More attention should be paid to the food and beverage choices available to elementary school children.

This statement asserts your position, but the terms more attention and food and beverage choices are vague.

Use specific language . You decide to explain what you mean about food and beverage choices , so you write:

Experts estimate that half of elementary school children consume nine times the recommended daily allowance of sugar.

This statement is specific, but it isn’t a thesis. It merely reports a statistic instead of making an assertion.

Make an assertion based on clearly stated support. You finally revise your thesis statement one more time to look like this:

Because half of all American elementary school children consume nine times the recommended daily allowance of sugar, schools should be required to replace the beverages in soda machines with healthy alternatives.

Notice how the thesis answers the question, “What should be done to reduce sugar consumption by children, and who should do it?” When you started thinking about the paper, you may not have had a specific question in mind, but as you became more involved in the topic, your ideas became more specific. Your thesis changed to reflect your new insights.

How to Tell a Strong Thesis Statement from a Weak One

1. a strong thesis statement takes some sort of stand..

Remember that your thesis needs to show your conclusions about a subject. For example, if you are writing a paper for a class on fitness, you might be asked to choose a popular weight-loss product to evaluate. Here are two thesis statements:

There are some negative and positive aspects to the Banana Herb Tea Supplement.

This is a weak thesis statement. First, it fails to take a stand. Second, the phrase negative and positive aspects is vague.

Because Banana Herb Tea Supplement promotes rapid weight loss that results in the loss of muscle and lean body mass, it poses a potential danger to customers.

This is a strong thesis because it takes a stand, and because it's specific.

2. A strong thesis statement justifies discussion.

Your thesis should indicate the point of the discussion. If your assignment is to write a paper on kinship systems, using your own family as an example, you might come up with either of these two thesis statements:

My family is an extended family.

This is a weak thesis because it merely states an observation. Your reader won’t be able to tell the point of the statement, and will probably stop reading.

While most American families would view consanguineal marriage as a threat to the nuclear family structure, many Iranian families, like my own, believe that these marriages help reinforce kinship ties in an extended family.

This is a strong thesis because it shows how your experience contradicts a widely-accepted view. A good strategy for creating a strong thesis is to show that the topic is controversial. Readers will be interested in reading the rest of the essay to see how you support your point.

3. A strong thesis statement expresses one main idea.

Readers need to be able to see that your paper has one main point. If your thesis statement expresses more than one idea, then you might confuse your readers about the subject of your paper. For example:

Companies need to exploit the marketing potential of the Internet, and Web pages can provide both advertising and customer support.

This is a weak thesis statement because the reader can’t decide whether the paper is about marketing on the Internet or Web pages. To revise the thesis, the relationship between the two ideas needs to become more clear. One way to revise the thesis would be to write:

Because the Internet is filled with tremendous marketing potential, companies should exploit this potential by using Web pages that offer both advertising and customer support.

This is a strong thesis because it shows that the two ideas are related. Hint: a great many clear and engaging thesis statements contain words like because , since , so , although , unless , and however .

4. A strong thesis statement is specific.

A thesis statement should show exactly what your paper will be about, and will help you keep your paper to a manageable topic. For example, if you're writing a seven-to-ten page paper on hunger, you might say:

World hunger has many causes and effects.

This is a weak thesis statement for two major reasons. First, world hunger can’t be discussed thoroughly in seven to ten pages. Second, many causes and effects is vague. You should be able to identify specific causes and effects. A revised thesis might look like this:

Hunger persists in Glandelinia because jobs are scarce and farming in the infertile soil is rarely profitable.

This is a strong thesis statement because it narrows the subject to a more specific and manageable topic, and it also identifies the specific causes for the existence of hunger.

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How to write an undergraduate-level essay

• 2. Create a preliminary document plan

Draft your thesis statement

• 4a. Become familiar with information sources
• 4b. Select the appropriate search tool
• 4c. Develop effective searches
• 4d. Beyond keyword searching
• 4e. Find statistical information
• 4f. Evaluate the resources you find
• 4g. Read, absorb, and organize the information you find
• 5. Create the final version of your document plan
• 6. Double-check your research
• 7. Start writing the first draft
• 8. Overcome writer's block
• 9. Revise the draft
• 10. Edit the draft
• 11. Prepare the final version
• 12. Submit the assignment

The benefit to having a strong thesis statement is that it gives you and your reader clear boundaries of what will be discussed within the paper. For example, if my thesis statement is that Royal Roads University is unique amongst post-secondary institutions on Vancouver Island because of its history, diversity wildlife, Hatley Park, and educational programs, I know exactly what I need to prove to my reader within the document. I have the four body paragraphs or perhaps four sections for my essay: history, diversity of wildlife, Hatley Park, and educational programs.

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Think about how you would describe your paper to a stranger in one or two sentences. "What's your paper about?", asks the stranger. You know that the person doesn't want a detailed description that would take many sentences to complete. How would you answer that question in one or two sentences?

If you're having a hard time writing it out, have an real conversation with someone so that you can describe your paper. Have your conversation partner ask you questions if he or she doesn't understand the focus of the paper until you have that one or two sentence description. Remember to keep in mind the assignment description so that you can be sure that your thesis statement is on track with the outcomes for the essay.

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Writing a First Draft

Now that you have a topic and/or a working thesis, you have several options for how to begin writing a more complete draft.

Just write. You already have at least one focusing idea. Start there. What do you want to say about it? What connections can you make with it? If you have a working thesis, what points might you make that support that thesis?

Make an outline. Write your topic or thesis down and then jot down what points you might make that will flesh out that topic or support that thesis. These don’t have to be detailed. In fact, they don’t even have to be complete sentences (yet)!

Begin with research. If this is an assignment that asks you to do research to support your points or to learn more about your topic, doing that research is an important early step (see the section on “ Finding Quality Texts ” in the “Information Literacy” section). This might include a range of things, such as conducting an interview, creating and administering a survey, or locating articles on the Internet and in library databases.

Research is a great early step because learning what information is available from credible sources about your topic can sometimes lead to shifting your thesis. Saving the research for a later step in the drafting process can mean making this change after already committing sometimes significant amounts of work to a thesis that existing credible research doesn’t support. Research is also useful because learning what information is available about your topic can help you flesh out what you might want to say about it.

Essay Structure

You might already be familiar with the five-paragraph essay structure, in which you spend the first paragraph introducing your topic, culminating in a thesis that has three distinct parts. That introduction paragraph is followed by three body paragraphs, each one of those going into some detail about one of the parts of the thesis. Finally, the conclusion paragraph summarizes the main ideas discussed in the essay and states the thesis (or a slightly re-worded version of the thesis) again.

This structure is commonly taught in high schools, and it has some pros and some cons.

• It helps get your thoughts organized.
• It is a good introduction to a simple way of structuring an essay that lets students focus on content rather than wrestling with a more complex structure.
• It familiarizes students with the general shape and components of many essays—a broader introductory conversation giving readers context for this discussion, followed by a more detailed supporting discussion in the body of the essay, and ending with a sense of wrapping up the discussion and refocusing on the main idea.
• It is an effective structure for in-class essays or timed written exams.
• It can be formulaic—essays structured this way sound a lot alike.
• It isn’t very flexible—often, topics don’t lend themselves easily to this structure.
• It doesn’t encourage research and discussion at the depth college-level work tends to ask for. Quite often, a paragraph is simply not enough space to have a conversation on paper that is thorough enough to support a stance presented in your thesis.

So, if the five-paragraph essay isn’t the golden ticket in college work, what is?

That is a trickier question! There isn’t really one prescribed structure that written college-level work adheres to—audience, purpose, length, and other considerations all help dictate what that structure will be for any given piece of writing you are doing. Instead, this text offers you some guidelines and best practices.

Things to Keep in Mind about Structure in College-Level Writing

Avoid the three-point structure.

Aim for a thesis that addresses a single issue rather than the three-point structure. Take a look at our example from the previous section, “ Finding the Thesis ”:

“Katniss Everdeen, the heroine of The Hunger Games, creates as much danger for herself as she faces from others over the course of the film.”

This thesis allows you to cover your single, narrow topic in greater depth, so you can examine multiple sides of a single angle of the topic rather than having to quickly and briefly address a broader main idea.

There’s No “Right” Number of Supporting Points

There is no prescribed number of supporting points. You don’t have to have three! Maybe you have two in great depth, or maybe four that explore that one element from the most salient angles. Depending on the length of your paper, you may even have more than that.

There’s More than One Good Spot for a Thesis

Depending on the goals of the assignment, your thesis may no longer sit at the end of the first paragraph, so let’s discuss a few places it can commonly be found in college writing.

It may end up at the end of your introductory information—once you’ve introduced your topic, given readers some reasonable context around it, and narrowed your focus to one area of that topic. This might put your thesis in the predictable end-of-the-first-paragraph spot, but it might also put that thesis several paragraphs into the paper

Some college work, particularly work that asks you to consider multiple sides of an issue fully, lends itself well to an end-of-paper thesis (sometimes called a “delayed thesis”). This thesis often appears a paragraph or so before the conclusion, which allows you to have a thorough discussion about multiple sides of a question and let that discussion guide you to your stance rather than having to spend the paper defending a stance you’ve already stated.

These are some common places you may find your thesis landing in your paper, but a thesis truly can be anywhere in a text.

Writing Beginnings

Beginnings have a few jobs. These will depend somewhat on the purpose of the writing, but here are some of the things the first couple of paragraphs do for your text:

• They establish the tone and primary audience of your text—is it casual? Academic? Geared toward a professional audience already versed in the topic? An interested audience that doesn’t know much about this topic yet?
• They introduce your audience to your topic.
• They give you an opportunity to provide context around that topic—what current conversations are happening around it? Why is it important? If it’s a topic your audience isn’t likely to know much about, you may find you need to define what the topic itself is.
• They let you show your audience what piece of that bigger topic you are going to be working with in this text and how you will be working with it.
• They might introduce a narrative, if appropriate, or a related story that provides an example of the topic being discussed.

Take a look at the thesis about Katniss once more. There are a number of discussions that you could have about this film, and almost as many that you could have about this film and its intersections with the concept of danger (such as corruption in government, the hazards of power, risks of love or other personal attachments, etc.). Your introduction moving toward this thesis will shift our attention to the prevalence of self-imposed danger in this film, which will narrow your reader’s focus in a way that prepares us for your thesis.

The most important thing at this point in the drafting process is to just get started, but when you’re ready, if you want to learn more about formulas and methods for writing introductions, see “ Writing Introductions ,” presented later in this section of the text.

Writing Middles

Middles tend to have a clearer job—they provide the meat of the discussion! Here are some ways that might happen:

• If you state a thesis early in the paper, the middle of the paper will likely provide support for that thesis.
• The middle might explore multiple sides of an issue.
• It might look at opposing views—ones other than the one you are supporting—and discuss why those don’t address the issue as well as the view you are supporting does.

Let’s think about the “multiple sides of the issue” approach to building support with our Hunger Games example. Perhaps Katniss may not see a particular dangerous situation she ends up in as being one she’s created, but another character or the viewers may disagree. It might be worth exploring both versions of this specific danger to give the most complete, balanced discussion to support your thesis.

Writing Endings

Endings, like beginnings, tend to have more than one job. Here are some things they often need to do for a text to feel complete:

• Reconnect to the main idea/thesis. However, note that this is different than a simple copy/paste of the thesis from earlier in the text. We’ve likely had a whole conversation in the text since we first encountered that thesis. Simply repeating it, or even replacing a few key words with synonyms, doesn’t acknowledge that bigger conversation. Instead, try pointing us back to the main idea in a new way.
• Tie up loose ends. If you opened the text with the beginning of a story to demonstrate how the topic applies to average daily life, the end of your text is a good time to share the end of that story with readers. If several ideas in the text tie together in a relevant way that didn’t fit neatly into the original discussion of those ideas, the end may be the place to do that.
• Keep the focus clear—this is your last chance to leave an impression on the reader. What do you want them to leave this text thinking about? What action do you want them to take? It’s often a good idea to be direct about this in the ending paragraph(s).

How might we reconnect with the main idea in our  Hunger Games example? We might say something like, “In many ways, Katniss Everdeen is her own greatest obstacle to the safe and peaceful life she seems to wish for.” It echoes, strongly, the original thesis, but also takes into account the more robust exploration that has happened in the middle parts of the paper.

As mentioned about writing introductions above, the most important thing at this point in the drafting process is to just get started (or in this case, to get started concluding), but when you’re ready, if you want to learn more about formulas and methods for writing conclusions, see “ Writing Conclusions ,” presented later in this section of the text.

The Word on College Reading and Writing Copyright © by Carol Burnell, Jaime Wood, Monique Babin, Susan Pesznecker, and Nicole Rosevear is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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How to Write a Thesis: Home

• Getting Started
• Choosing a Topic
• Creating an Outline
• Conducting Research
• Drafting a Thesis
• Revising a Thesis
• Creating Citations

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This guide features information and resources for each step of the research and writing process. You'll find material on how to:

• chose a topic
• create an outline
• craft a thesis statement
• draft all the components of a typical thesis (introduction, literature review, analysis, conclusion, etc.)
• revise a thesis for better clarity, structure, and organization
• create accurate citations and avoid plagiarism

General Resources

Purdue Online Writing Lab (OWL)   The Purdue OWL contains a wealth of articles on research, writing, and citation. They are especially well-known for their citation guides.

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What is a thesis | A Complete Guide with Examples

Table of Contents

A thesis is a comprehensive academic paper based on your original research that presents new findings, arguments, and ideas of your study. It’s typically submitted at the end of your master’s degree or as a capstone of your bachelor’s degree.

However, writing a thesis can be laborious, especially for beginners. From the initial challenge of pinpointing a compelling research topic to organizing and presenting findings, the process is filled with potential pitfalls.

Therefore, to help you, this guide talks about what is a thesis. Additionally, it offers revelations and methodologies to transform it from an overwhelming task to a manageable and rewarding academic milestone.

What is a thesis?

A thesis is an in-depth research study that identifies a particular topic of inquiry and presents a clear argument or perspective about that topic using evidence and logic.

Writing a thesis showcases your ability of critical thinking, gathering evidence, and making a compelling argument. Integral to these competencies is thorough research, which not only fortifies your propositions but also confers credibility to your entire study.

Furthermore, there's another phenomenon you might often confuse with the thesis: the ' working thesis .' However, they aren't similar and shouldn't be used interchangeably.

A working thesis, often referred to as a preliminary or tentative thesis, is an initial version of your thesis statement. It serves as a draft or a starting point that guides your research in its early stages.

As you research more and gather more evidence, your initial thesis (aka working thesis) might change. It's like a starting point that can be adjusted as you learn more. It's normal for your main topic to change a few times before you finalize it.

While a thesis identifies and provides an overarching argument, the key to clearly communicating the central point of that argument lies in writing a strong thesis statement.

What is a thesis statement?

A strong thesis statement (aka thesis sentence) is a concise summary of the main argument or claim of the paper. It serves as a critical anchor in any academic work, succinctly encapsulating the primary argument or main idea of the entire paper.

Typically found within the introductory section, a strong thesis statement acts as a roadmap of your thesis, directing readers through your arguments and findings. By delineating the core focus of your investigation, it offers readers an immediate understanding of the context and the gravity of your study.

Furthermore, an effectively crafted thesis statement can set forth the boundaries of your research, helping readers anticipate the specific areas of inquiry you are addressing.

Different types of thesis statements

A good thesis statement is clear, specific, and arguable. Therefore, it is necessary for you to choose the right type of thesis statement for your academic papers.

Thesis statements can be classified based on their purpose and structure. Here are the primary types of thesis statements:

Argumentative (or Persuasive) thesis statement

Purpose : To convince the reader of a particular stance or point of view by presenting evidence and formulating a compelling argument.

Example : Reducing plastic use in daily life is essential for environmental health.

Analytical thesis statement

Purpose : To break down an idea or issue into its components and evaluate it.

Example : By examining the long-term effects, social implications, and economic impact of climate change, it becomes evident that immediate global action is necessary.

Expository (or Descriptive) thesis statement

Purpose : To explain a topic or subject to the reader.

Example : The Great Depression, spanning the 1930s, was a severe worldwide economic downturn triggered by a stock market crash, bank failures, and reduced consumer spending.

Cause and effect thesis statement

Purpose : To demonstrate a cause and its resulting effect.

Example : Overuse of smartphones can lead to impaired sleep patterns, reduced face-to-face social interactions, and increased levels of anxiety.

Compare and contrast thesis statement

Purpose : To highlight similarities and differences between two subjects.

Example : "While both novels '1984' and 'Brave New World' delve into dystopian futures, they differ in their portrayal of individual freedom, societal control, and the role of technology."

When you write a thesis statement , it's important to ensure clarity and precision, so the reader immediately understands the central focus of your work.

What is the difference between a thesis and a thesis statement?

While both terms are frequently used interchangeably, they have distinct meanings.

A thesis refers to the entire research document, encompassing all its chapters and sections. In contrast, a thesis statement is a brief assertion that encapsulates the central argument of the research.

Here’s an in-depth differentiation table of a thesis and a thesis statement.

Now, to craft a compelling thesis, it's crucial to adhere to a specific structure. Let’s break down these essential components that make up a thesis structure

15 components of a thesis structure

Navigating a thesis can be daunting. However, understanding its structure can make the process more manageable.

Here are the key components or different sections of a thesis structure:

Your thesis begins with the title page. It's not just a formality but the gateway to your research.

Here, you'll prominently display the necessary information about you (the author) and your institutional details.

• Title of your thesis
• Your full name
• Your department
• Your institution and degree program
• Your submission date
• Your Supervisor's name (in some cases)
• Your Department or faculty (in some cases)
• Your University's logo (in some cases)
• Your Student ID (in some cases)

In a concise manner, you'll have to summarize the critical aspects of your research in typically no more than 200-300 words.

This includes the problem statement, methodology, key findings, and conclusions. For many, the abstract will determine if they delve deeper into your work, so ensure it's clear and compelling.

Acknowledgments

Research is rarely a solitary endeavor. In the acknowledgments section, you have the chance to express gratitude to those who've supported your journey.

This might include advisors, peers, institutions, or even personal sources of inspiration and support. It's a personal touch, reflecting the humanity behind the academic rigor.

Table of contents

A roadmap for your readers, the table of contents lists the chapters, sections, and subsections of your thesis.

By providing page numbers, you allow readers to navigate your work easily, jumping to sections that pique their interest.

List of figures and tables

Research often involves data, and presenting this data visually can enhance understanding. This section provides an organized listing of all figures and tables in your thesis.

It's a visual index, ensuring that readers can quickly locate and reference your graphical data.

Introduction

Here's where you introduce your research topic, articulate the research question or objective, and outline the significance of your study.

• Present the research topic : Clearly articulate the central theme or subject of your research.
• Background information : Ground your research topic, providing any necessary context or background information your readers might need to understand the significance of your study.
• Define the scope : Clearly delineate the boundaries of your research, indicating what will and won't be covered.
• Literature review : Introduce any relevant existing research on your topic, situating your work within the broader academic conversation and highlighting where your research fits in.
• State the research Question(s) or objective(s) : Clearly articulate the primary questions or objectives your research aims to address.
• Outline the study's structure : Give a brief overview of how the subsequent sections of your work will unfold, guiding your readers through the journey ahead.

The introduction should captivate your readers, making them eager to delve deeper into your research journey.

Literature review section

Your study correlates with existing research. Therefore, in the literature review section, you'll engage in a dialogue with existing knowledge, highlighting relevant studies, theories, and findings.

It's here that you identify gaps in the current knowledge, positioning your research as a bridge to new insights.

To streamline this process, consider leveraging AI tools. For example, the SciSpace literature review tool enables you to efficiently explore and delve into research papers, simplifying your literature review journey.

Methodology

In the research methodology section, you’ll detail the tools, techniques, and processes you employed to gather and analyze data. This section will inform the readers about how you approached your research questions and ensures the reproducibility of your study.

Here's a breakdown of what it should encompass:

• Research Design : Describe the overall structure and approach of your research. Are you conducting a qualitative study with in-depth interviews? Or is it a quantitative study using statistical analysis? Perhaps it's a mixed-methods approach?
• Data Collection : Detail the methods you used to gather data. This could include surveys, experiments, observations, interviews, archival research, etc. Mention where you sourced your data, the duration of data collection, and any tools or instruments used.
• Sampling : If applicable, explain how you selected participants or data sources for your study. Discuss the size of your sample and the rationale behind choosing it.
• Data Analysis : Describe the techniques and tools you used to process and analyze the data. This could range from statistical tests in quantitative research to thematic analysis in qualitative research.
• Validity and Reliability : Address the steps you took to ensure the validity and reliability of your findings to ensure that your results are both accurate and consistent.
• Ethical Considerations : Highlight any ethical issues related to your research and the measures you took to address them, including — informed consent, confidentiality, and data storage and protection measures.

Moreover, different research questions necessitate different types of methodologies. For instance:

• Experimental methodology : Often used in sciences, this involves a controlled experiment to discern causality.
• Qualitative methodology : Employed when exploring patterns or phenomena without numerical data. Methods can include interviews, focus groups, or content analysis.
• Quantitative methodology : Concerned with measurable data and often involves statistical analysis. Surveys and structured observations are common tools here.
• Mixed methods : As the name implies, this combines both qualitative and quantitative methodologies.

The Methodology section isn’t just about detailing the methods but also justifying why they were chosen. The appropriateness of the methods in addressing your research question can significantly impact the credibility of your findings.

Results (or Findings)

This section presents the outcomes of your research. It's crucial to note that the nature of your results may vary; they could be quantitative, qualitative, or a mix of both.

Quantitative results often present statistical data, showcasing measurable outcomes, and they benefit from tables, graphs, and figures to depict these data points.

Qualitative results , on the other hand, might delve into patterns, themes, or narratives derived from non-numerical data, such as interviews or observations.

Regardless of the nature of your results, clarity is essential. This section is purely about presenting the data without offering interpretations — that comes later in the discussion.

In the discussion section, the raw data transforms into valuable insights.

Start by revisiting your research question and contrast it with the findings. How do your results expand, constrict, or challenge current academic conversations?

Dive into the intricacies of the data, guiding the reader through its implications. Detail potential limitations transparently, signaling your awareness of the research's boundaries. This is where your academic voice should be resonant and confident.

Practical implications (Recommendation) section

Based on the insights derived from your research, this section provides actionable suggestions or proposed solutions.

Whether aimed at industry professionals or the general public, recommendations translate your academic findings into potential real-world actions. They help readers understand the practical implications of your work and how it can be applied to effect change or improvement in a given field.

When crafting recommendations, it's essential to ensure they're feasible and rooted in the evidence provided by your research. They shouldn't merely be aspirational but should offer a clear path forward, grounded in your findings.

The conclusion provides closure to your research narrative.

It's not merely a recap but a synthesis of your main findings and their broader implications. Reconnect with the research questions or hypotheses posited at the beginning, offering clear answers based on your findings.

Reflect on the broader contributions of your study, considering its impact on the academic community and potential real-world applications.

Lastly, the conclusion should leave your readers with a clear understanding of the value and impact of your study.

References (or Bibliography)

Every theory you've expounded upon, every data point you've cited, and every methodological precedent you've followed finds its acknowledgment here.

In references, it's crucial to ensure meticulous consistency in formatting, mirroring the specific guidelines of the chosen citation style .

Proper referencing helps to avoid plagiarism , gives credit to original ideas, and allows readers to explore topics of interest. Moreover, it situates your work within the continuum of academic knowledge.

To properly cite the sources used in the study, you can rely on online citation generator tools  to generate accurate citations!

Here’s more on how you can cite your sources.

Often, the depth of research produces a wealth of material that, while crucial, can make the core content of the thesis cumbersome. The appendix is where you mention extra information that supports your research but isn't central to the main text.

Whether it's raw datasets, detailed procedural methodologies, extended case studies, or any other ancillary material, the appendices ensure that these elements are archived for reference without breaking the main narrative's flow.

For thorough researchers and readers keen on meticulous details, the appendices provide a treasure trove of insights.

Glossary (optional)

In academics, specialized terminologies, and jargon are inevitable. However, not every reader is versed in every term.

The glossary, while optional, is a critical tool for accessibility. It's a bridge ensuring that even readers from outside the discipline can access, understand, and appreciate your work.

By defining complex terms and providing context, you're inviting a wider audience to engage with your research, enhancing its reach and impact.

Remember, while these components provide a structured framework, the essence of your thesis lies in the originality of your ideas, the rigor of your research, and the clarity of your presentation.

As you craft each section, keep your readers in mind, ensuring that your passion and dedication shine through every page.

Thesis examples

To further elucidate the concept of a thesis, here are illustrative examples from various fields:

Example 1 (History): Abolition, Africans, and Abstraction: the Influence of the ‘Noble Savage’ on British and French Antislavery Thought, 1787-1807 by Suchait Kahlon.

Example 2 (Climate Dynamics): Influence of external forcings on abrupt millennial-scale climate changes: a statistical modelling study by Takahito Mitsui · Michel Crucifix

Checklist for your thesis evaluation

Evaluating your thesis ensures that your research meets the standards of academia. Here's an elaborate checklist to guide you through this critical process.

Content and structure

• Is the thesis statement clear, concise, and debatable?
• Does the introduction provide sufficient background and context?
• Is the literature review comprehensive, relevant, and well-organized?
• Does the methodology section clearly describe and justify the research methods?
• Are the results/findings presented clearly and logically?
• Does the discussion interpret the results in light of the research question and existing literature?
• Is the conclusion summarizing the research and suggesting future directions or implications?

Clarity and coherence

• Is the writing clear and free of jargon?
• Are ideas and sections logically connected and flowing?
• Is there a clear narrative or argument throughout the thesis?

Research quality

• Is the research question significant and relevant?
• Are the research methods appropriate for the question?
• Is the sample size (if applicable) adequate?
• Are the data analysis techniques appropriate and correctly applied?
• Are potential biases or limitations addressed?

Originality and significance

• Does the thesis contribute new knowledge or insights to the field?
• Is the research grounded in existing literature while offering fresh perspectives?

Formatting and presentation

• Is the thesis formatted according to institutional guidelines?
• Are figures, tables, and charts clear, labeled, and referenced in the text?
• Is the bibliography or reference list complete and consistently formatted?
• Are appendices relevant and appropriately referenced in the main text?

Grammar and language

• Is the thesis free of grammatical and spelling errors?
• Is the language professional, consistent, and appropriate for an academic audience?
• Are quotations and paraphrased material correctly cited?

Feedback and revision

• Have you sought feedback from peers, advisors, or experts in the field?
• Have you addressed the feedback and made the necessary revisions?

Overall assessment

• Does the thesis as a whole feel cohesive and comprehensive?
• Would the thesis be understandable and valuable to someone in your field?

Ensure to use this checklist to leave no ground for doubt or missed information in your thesis.

After writing your thesis, the next step is to discuss and defend your findings verbally in front of a knowledgeable panel. You’ve to be well prepared as your professors may grade your presentation abilities.

Preparing your thesis defense

A thesis defense, also known as "defending the thesis," is the culmination of a scholar's research journey. It's the final frontier, where you’ll present their findings and face scrutiny from a panel of experts.

Typically, the defense involves a public presentation where you’ll have to outline your study, followed by a question-and-answer session with a committee of experts. This committee assesses the validity, originality, and significance of the research.

The defense serves as a rite of passage for scholars. It's an opportunity to showcase expertise, address criticisms, and refine arguments. A successful defense not only validates the research but also establishes your authority as a researcher in your field.

Here’s how you can effectively prepare for your thesis defense .

Now, having touched upon the process of defending a thesis, it's worth noting that scholarly work can take various forms, depending on academic and regional practices.

One such form, often paralleled with the thesis, is the 'dissertation.' But what differentiates the two?

Dissertation vs. Thesis

Often used interchangeably in casual discourse, they refer to distinct research projects undertaken at different levels of higher education.

To the uninitiated, understanding their meaning might be elusive. So, let's demystify these terms and delve into their core differences.

Here's a table differentiating between the two.

Wrapping up

From understanding the foundational concept of a thesis to navigating its various components, differentiating it from a dissertation, and recognizing the importance of proper citation — this guide covers it all.

As scholars and readers, understanding these nuances not only aids in academic pursuits but also fosters a deeper appreciation for the relentless quest for knowledge that drives academia.

It’s important to remember that every thesis is a testament to curiosity, dedication, and the indomitable spirit of discovery.

Good luck with your thesis writing!

Frequently Asked Questions

A thesis typically ranges between 40-80 pages, but its length can vary based on the research topic, institution guidelines, and level of study.

A PhD thesis usually spans 200-300 pages, though this can vary based on the discipline, complexity of the research, and institutional requirements.

To identify a thesis topic, consider current trends in your field, gaps in existing literature, personal interests, and discussions with advisors or mentors. Additionally, reviewing related journals and conference proceedings can provide insights into potential areas of exploration.

The conceptual framework is often situated in the literature review or theoretical framework section of a thesis. It helps set the stage by providing the context, defining key concepts, and explaining the relationships between variables.

A thesis statement should be concise, clear, and specific. It should state the main argument or point of your research. Start by pinpointing the central question or issue your research addresses, then condense that into a single statement, ensuring it reflects the essence of your paper.

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8.3 Drafting

Learning objectives.

• Identify drafting strategies that improve writing.
• Use drafting strategies to prepare the first draft of an essay.

Drafting is the stage of the writing process in which you develop a complete first version of a piece of writing.

Even professional writers admit that an empty page scares them because they feel they need to come up with something fresh and original every time they open a blank document on their computers. Because you have completed the first two steps in the writing process, you have already recovered from empty page syndrome. You have hours of prewriting and planning already done. You know what will go on that blank page: what you wrote in your outline.

Getting Started: Strategies For Drafting

Your objective for this portion of Chapter 8 “The Writing Process: How Do I Begin?” is to draft the body paragraphs of a standard five-paragraph essay. A five-paragraph essay contains an introduction, three body paragraphs, and a conclusion. If you are more comfortable starting on paper than on the computer, you can start on paper and then type it before you revise. You can also use a voice recorder to get yourself started, dictating a paragraph or two to get you thinking. In this lesson, Mariah does all her work on the computer, but you may use pen and paper or the computer to write a rough draft.

Making the Writing Process Work for You

What makes the writing process so beneficial to writers is that it encourages alternatives to standard practices while motivating you to develop your best ideas. For instance, the following approaches, done alone or in combination with others, may improve your writing and help you move forward in the writing process:

• Begin writing with the part you know the most about. You can start with the third paragraph in your outline if ideas come easily to mind. You can start with the second paragraph or the first paragraph, too. Although paragraphs may vary in length, keep in mind that short paragraphs may contain insufficient support. Readers may also think the writing is abrupt. Long paragraphs may be wordy and may lose your reader’s interest. As a guideline, try to write paragraphs longer than one sentence but shorter than the length of an entire double-spaced page.
• Write one paragraph at a time and then stop. As long as you complete the assignment on time, you may choose how many paragraphs you complete in one sitting. Pace yourself. On the other hand, try not to procrastinate. Writers should always meet their deadlines.
• Take short breaks to refresh your mind. This tip might be most useful if you are writing a multipage report or essay. Still, if you are antsy or cannot concentrate, take a break to let your mind rest. But do not let breaks extend too long. If you spend too much time away from your essay, you may have trouble starting again. You may forget key points or lose momentum. Try setting an alarm to limit your break, and when the time is up, return to your desk to write.
• Be reasonable with your goals. If you decide to take ten-minute breaks, try to stick to that goal. If you told yourself that you need more facts, then commit to finding them. Holding yourself to your own goals will create successful writing assignments.
• Keep your audience and purpose in mind as you write. These aspects of writing are just as important when you are writing a single paragraph for your essay as when you are considering the direction of the entire essay.

Of all of these considerations, keeping your purpose and your audience at the front of your mind is the most important key to writing success. If your purpose is to persuade, for example, you will present your facts and details in the most logical and convincing way you can.

Your purpose will guide your mind as you compose your sentences. Your audience will guide word choice. Are you writing for experts, for a general audience, for other college students, or for people who know very little about your topic? Keep asking yourself what your readers, with their background and experience, need to be told in order to understand your ideas. How can you best express your ideas so they are totally clear and your communication is effective?

You may want to identify your purpose and audience on an index card that you clip to your paper (or keep next to your computer). On that card, you may want to write notes to yourself—perhaps about what that audience might not know or what it needs to know—so that you will be sure to address those issues when you write. It may be a good idea to also state exactly what you want to explain to that audience, or to inform them of, or to persuade them about.

Writing at Work

Many of the documents you produce at work target a particular audience for a particular purpose. You may find that it is highly advantageous to know as much as you can about your target audience and to prepare your message to reach that audience, even if the audience is a coworker or your boss. Menu language is a common example. Descriptions like “organic romaine” and “free-range chicken” are intended to appeal to a certain type of customer though perhaps not to the same customer who craves a thick steak. Similarly, mail-order companies research the demographics of the people who buy their merchandise. Successful vendors customize product descriptions in catalogs to appeal to their buyers’ tastes. For example, the product descriptions in a skateboarder catalog will differ from the descriptions in a clothing catalog for mature adults.

Using the topic for the essay that you outlined in Section 8.2 “Outlining” , describe your purpose and your audience as specifically as you can. Use your own sheet of paper to record your responses. Then keep these responses near you during future stages of the writing process.

My purpose: ____________________________________________

____________________________________________

My audience: ____________________________________________

Setting Goals for Your First Draft

A draft is a complete version of a piece of writing, but it is not the final version. The step in the writing process after drafting, as you may remember, is revising. During revising, you will have the opportunity to make changes to your first draft before you put the finishing touches on it during the editing and proofreading stage. A first draft gives you a working version that you can later improve.

Workplace writing in certain environments is done by teams of writers who collaborate on the planning, writing, and revising of documents, such as long reports, technical manuals, and the results of scientific research. Collaborators do not need to be in the same room, the same building, or even the same city. Many collaborations are conducted over the Internet.

In a perfect collaboration, each contributor has the right to add, edit, and delete text. Strong communication skills, in addition to strong writing skills, are important in this kind of writing situation because disagreements over style, content, process, emphasis, and other issues may arise.

The collaborative software, or document management systems, that groups use to work on common projects is sometimes called groupware or workgroup support systems.

The reviewing tool on some word-processing programs also gives you access to a collaborative tool that many smaller workgroups use when they exchange documents. You can also use it to leave comments to yourself.

If you invest some time now to investigate how the reviewing tool in your word processor works, you will be able to use it with confidence during the revision stage of the writing process. Then, when you start to revise, set your reviewing tool to track any changes you make, so you will be able to tinker with text and commit only those final changes you want to keep.

Discovering the Basic Elements of a First Draft

If you have been using the information in this chapter step by step to help you develop an assignment, you already have both a formal topic outline and a formal sentence outline to direct your writing. Knowing what a first draft looks like will help you make the creative leap from the outline to the first draft. A first draft should include the following elements:

• An introduction that piques the audience’s interest, tells what the essay is about, and motivates readers to keep reading.
• A thesis statement that presents the main point, or controlling idea, of the entire piece of writing.
• A topic sentence in each paragraph that states the main idea of the paragraph and implies how that main idea connects to the thesis statement.
• Supporting sentences in each paragraph that develop or explain the topic sentence. These can be specific facts, examples, anecdotes, or other details that elaborate on the topic sentence.
• A conclusion that reinforces the thesis statement and leaves the audience with a feeling of completion.

These elements follow the standard five-paragraph essay format, which you probably first encountered in high school. This basic format is valid for most essays you will write in college, even much longer ones. For now, however, Mariah focuses on writing the three body paragraphs from her outline. Chapter 9 “Writing Essays: From Start to Finish” covers writing introductions and conclusions, and you will read Mariah’s introduction and conclusion in Chapter 9 “Writing Essays: From Start to Finish” .

The Role of Topic Sentences

Topic sentences make the structure of a text and the writer’s basic arguments easy to locate and comprehend. In college writing, using a topic sentence in each paragraph of the essay is the standard rule. However, the topic sentence does not always have to be the first sentence in your paragraph even if it the first item in your formal outline.

When you begin to draft your paragraphs, you should follow your outline fairly closely. After all, you spent valuable time developing those ideas. However, as you begin to express your ideas in complete sentences, it might strike you that the topic sentence might work better at the end of the paragraph or in the middle. Try it. Writing a draft, by its nature, is a good time for experimentation.

The topic sentence can be the first, middle, or final sentence in a paragraph. The assignment’s audience and purpose will often determine where a topic sentence belongs. When the purpose of the assignment is to persuade, for example, the topic sentence should be the first sentence in a paragraph. In a persuasive essay, the writer’s point of view should be clearly expressed at the beginning of each paragraph.

Choosing where to position the topic sentence depends not only on your audience and purpose but also on the essay’s arrangement, or order. When you organize information according to order of importance, the topic sentence may be the final sentence in a paragraph. All the supporting sentences build up to the topic sentence. Chronological order may also position the topic sentence as the final sentence because the controlling idea of the paragraph may make the most sense at the end of a sequence.

When you organize information according to spatial order, a topic sentence may appear as the middle sentence in a paragraph. An essay arranged by spatial order often contains paragraphs that begin with descriptions. A reader may first need a visual in his or her mind before understanding the development of the paragraph. When the topic sentence is in the middle, it unites the details that come before it with the ones that come after it.

As you read critically throughout the writing process, keep topic sentences in mind. You may discover topic sentences that are not always located at the beginning of a paragraph. For example, fiction writers customarily use topic ideas, either expressed or implied, to move readers through their texts. In nonfiction writing, such as popular magazines, topic sentences are often used when the author thinks it is appropriate (based on the audience and the purpose, of course). A single topic sentence might even control the development of a number of paragraphs. For more information on topic sentences, please see Chapter 6 “Writing Paragraphs: Separating Ideas and Shaping Content” .

Developing topic sentences and thinking about their placement in a paragraph will prepare you to write the rest of the paragraph.

The paragraph is the main structural component of an essay as well as other forms of writing. Each paragraph of an essay adds another related main idea to support the writer’s thesis, or controlling idea. Each related main idea is supported and developed with facts, examples, and other details that explain it. By exploring and refining one main idea at a time, writers build a strong case for their thesis.

Paragraph Length

How long should a paragraph be?

One answer to this important question may be “long enough”—long enough for you to address your points and explain your main idea. To grab attention or to present succinct supporting ideas, a paragraph can be fairly short and consist of two to three sentences. A paragraph in a complex essay about some abstract point in philosophy or archaeology can be three-quarters of a page or more in length. As long as the writer maintains close focus on the topic and does not ramble, a long paragraph is acceptable in college-level writing. In general, try to keep the paragraphs longer than one sentence but shorter than one full page of double-spaced text.

Journalistic style often calls for brief two- or three-sentence paragraphs because of how people read the news, both online and in print. Blogs and other online information sources often adopt this paragraphing style, too. Readers often skim the first paragraphs of a great many articles before settling on the handful of stories they want to read in detail.

You may find that a particular paragraph you write may be longer than one that will hold your audience’s interest. In such cases, you should divide the paragraph into two or more shorter paragraphs, adding a topic statement or some kind of transitional word or phrase at the start of the new paragraph. Transition words or phrases show the connection between the two ideas.

In all cases, however, be guided by what you instructor wants and expects to find in your draft. Many instructors will expect you to develop a mature college-level style as you progress through the semester’s assignments.

To build your sense of appropriate paragraph length, use the Internet to find examples of the following items. Copy them into a file, identify your sources, and present them to your instructor with your annotations, or notes.

• A news article written in short paragraphs. Take notes on, or annotate, your selection with your observations about the effect of combining paragraphs that develop the same topic idea. Explain how effective those paragraphs would be.
• A long paragraph from a scholarly work that you identify through an academic search engine. Annotate it with your observations about the author’s paragraphing style.

Starting Your First Draft

Now we are finally ready to look over Mariah’s shoulder as she begins to write her essay about digital technology and the confusing choices that consumers face. As she does, you should have in front of you your outline, with its thesis statement and topic sentences, and the notes you wrote earlier in this lesson on your purpose and audience. Reviewing these will put both you and Mariah in the proper mind-set to start.

The following is Mariah’s thesis statement.

Here are the notes that Mariah wrote to herself to characterize her purpose and audience.

Mariah chose to begin by writing a quick introduction based on her thesis statement. She knew that she would want to improve her introduction significantly when she revised. Right now, she just wanted to give herself a starting point. You will read her introduction again in Section 8.4 “Revising and Editing” when she revises it.

Remember Mariah’s other options. She could have started directly with any of the body paragraphs.

You will learn more about writing attention-getting introductions and effective conclusions in Chapter 9 “Writing Essays: From Start to Finish” .

With her thesis statement and her purpose and audience notes in front of her, Mariah then looked at her sentence outline. She chose to use that outline because it includes the topic sentences. The following is the portion of her outline for the first body paragraph. The roman numeral II identifies the topic sentence for the paragraph, capital letters indicate supporting details, and arabic numerals label subpoints.

Mariah then began to expand the ideas in her outline into a paragraph. Notice how the outline helped her guarantee that all her sentences in the body of the paragraph develop the topic sentence.

If you write your first draft on the computer, consider creating a new file folder for each course with a set of subfolders inside the course folders for each assignment you are given. Label the folders clearly with the course names, and label each assignment folder and word processing document with a title that you will easily recognize. The assignment name is a good choice for the document. Then use that subfolder to store all the drafts you create. When you start each new draft, do not just write over the last one. Instead, save the draft with a new tag after the title—draft 1, draft 2, and so on—so that you will have a complete history of drafts in case your instructor wishes you to submit them.

In your documents, observe any formatting requirements—for margins, headers, placement of page numbers, and other layout matters—that your instructor requires.

Study how Mariah made the transition from her sentence outline to her first draft. First, copy her outline onto your own sheet of paper. Leave a few spaces between each part of the outline. Then copy sentences from Mariah’s paragraph to align each sentence with its corresponding entry in her outline.

Continuing the First Draft

Mariah continued writing her essay, moving to the second and third body paragraphs. She had supporting details but no numbered subpoints in her outline, so she had to consult her prewriting notes for specific information to include.

If you decide to take a break between finishing your first body paragraph and starting the next one, do not start writing immediately when you return to your work. Put yourself back in context and in the mood by rereading what you have already written. This is what Mariah did. If she had stopped writing in the middle of writing the paragraph, she could have jotted down some quick notes to herself about what she would write next.

Preceding each body paragraph that Mariah wrote is the appropriate section of her sentence outline. Notice how she expanded roman numeral III from her outline into a first draft of the second body paragraph. As you read, ask yourself how closely she stayed on purpose and how well she paid attention to the needs of her audience.

Mariah then began her third and final body paragraph using roman numeral IV from her outline.

Reread body paragraphs two and three of the essay that Mariah is writing. Then answer the questions on your own sheet of paper.

• In body paragraph two, Mariah decided to develop her paragraph as a nonfiction narrative. Do you agree with her decision? Explain. How else could she have chosen to develop the paragraph? Why is that better?
• Compare the writing styles of paragraphs two and three. What evidence do you have that Mariah was getting tired or running out of steam? What advice would you give her? Why?
• Choose one of these two body paragraphs. Write a version of your own that you think better fits Mariah’s audience and purpose.

Writing a Title

A writer’s best choice for a title is one that alludes to the main point of the entire essay. Like the headline in a newspaper or the big, bold title in a magazine, an essay’s title gives the audience a first peek at the content. If readers like the title, they are likely to keep reading.

Following her outline carefully, Mariah crafted each paragraph of her essay. Moving step by step in the writing process, Mariah finished the draft and even included a brief concluding paragraph (you will read her conclusion in Chapter 9 “Writing Essays: From Start to Finish” ). She then decided, as the final touch for her writing session, to add an engaging title.

Writing Your Own First Draft

Now you may begin your own first draft, if you have not already done so. Follow the suggestions and the guidelines presented in this section.

Key Takeaways

• Make the writing process work for you. Use any and all of the strategies that help you move forward in the writing process.
• Always be aware of your purpose for writing and the needs of your audience. Cater to those needs in every sensible way.
• Remember to include all the key structural parts of an essay: a thesis statement that is part of your introductory paragraph, three or more body paragraphs as described in your outline, and a concluding paragraph. Then add an engaging title to draw in readers.
• Write paragraphs of an appropriate length for your writing assignment. Paragraphs in college-level writing can be a page long, as long as they cover the main topics in your outline.
• Use your topic outline or your sentence outline to guide the development of your paragraphs and the elaboration of your ideas. Each main idea, indicated by a roman numeral in your outline, becomes the topic of a new paragraph. Develop it with the supporting details and the subpoints of those details that you included in your outline.
• Generally speaking, write your introduction and conclusion last, after you have fleshed out the body paragraphs.

Writing for Success Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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How to Draft a Thesis Proposal

Last Updated: April 2, 2021 References

This article was co-authored by wikiHow Staff . Our trained team of editors and researchers validate articles for accuracy and comprehensiveness. wikiHow's Content Management Team carefully monitors the work from our editorial staff to ensure that each article is backed by trusted research and meets our high quality standards. There are 7 references cited in this article, which can be found at the bottom of the page. This article has been viewed 36,498 times.

A thesis proposal is the gateway to your actual research work. Your proposal will show your committee that you have a concrete plan for pursuing significant research that will contribute something interesting to your field. Once your proposal is approved, you will have a blueprint for your work. Define your topic, outline your proposal, and proofread to help make the process manageable.

Defining Your Topic

• For example, you can start with the general idea that you want to write about how children are portrayed in 19th century Russian literature and your favorite authors are Turgenev and Tolstoy.

• Your working title should be longer and more descriptive than your final title.
• For example, your working title could be “Tracing the Portrayal of Children in 19th Century Russian Literature in the Works of Turgenev and Tolstoy.”

• Use the literature review to synthesize information about previous work done on your topic or topics related to yours.
• Find the strengths and weaknesses in earlier studies that can be improved by your thesis.
• Reviewing previous literature will help you figure out the potential significance of your thesis.
• For example, you will want to start by looking for other works on Turgenev and Tolstoy as well as works that discuss the portrayal of children in 19th century Russian literature.

Outlining Your Proposal

• Generally, a thesis proposal has an introduction, an abstract, a literature review, a discussion about methodology and theory, a timeline, and a bibliography.
• Depending on your field, you may also present preliminary data and include appendices.

• You do not need to know everything at this point. This is just to get you started.
• Specific novels or stories you will focus on.
• Why you believe Turgenev and Tolstoy should be examined together.
• How you will approach different genres.
• How you will treat different stages of each author’s career.

• This level of detail will save you time when writing the proposal.
• You can also come up with a preliminary list of chapters.

Pulling It Together

• For example, your introduction might start like: “Turgenev and Tolstoy wrote about memorable children, women, and peasants. While there are many studies about portrayals of women and peasants in their novels, there is much less scholarship about how children are depicted. This thesis will address children in these authors’ works.”

• For example, you could say something like: “Eikhenbaum and Berlin’s standard studies of Tolstoy will provide background information to this study. Neither of these works addresses any children who appear in Tolstoy’s novels.”

• Address any possible concerns the committee might have with your methods and explain why you chose those methods.
• This is where you should include information about working with human subjects or other data collection that might require additional permissions.
• Information and discussion about initial data can be a separate section.

• Provide dates wherever possible.

• Write your abstract last when you can best summarize your thesis. [7] X Research source

Expert Q&A

• The proposal will end with a bibliography and any appendices. Thanks Helpful 0 Not Helpful 0
• Choose a standard citation style and write the bibliography and endnotes accordingly. Thanks Helpful 0 Not Helpful 0
• Keep in mind that the proposal is not a contract, but a starting point. Any aspect of your thesis might change as you begin working on it. Thanks Helpful 0 Not Helpful 0

You Might Also Like

• ↑ https://student.unsw.edu.au/thesis-proposals
• ↑ http://advice.writing.utoronto.ca/types-of-writing/literature-review/
• ↑ http://www.uvm.edu/~aivakhiv/thesisoutline.pdf
• ↑ http://writingcenter.unc.edu/handouts/literature-reviews/
• ↑ https://www.unk.edu/academics/gradstudies/admissions/grad-files/Grad%20Files/ThesisGdlnsFinal08.pdf
• ↑ https://www.sfu.ca/~jcnesbit/HowToWriteAbstract.htm
• ↑ http://www.ldeo.columbia.edu/~martins/sen_res/how_to_thesis_proposal.html

About this article

When you draft your thesis proposal, you’ll want to define your topic and outline your proposed research. Start with a working title, which is a short statement about your thesis. It should briefly state what your research topic is and how you plan to conduct your research. Then, you can begin outlining your proposal. Begin with an introduction that discusses the significance of the problem you’re addressing with your thesis and what it will contribute to the field. You’ll also want to state your research objectives and the questions you plan to address. Another aspect of your proposal is your literature review, which should summarize the important information you found in your sources and analyze how it impacts the work you plan to do. Next, discuss your theoretical approach and the methodologies you plan to use to conduct your research. Include a timeline that outlines how long each part of your proposal will take you to complete. While your abstract will typically come at the start of your proposal, you may want to write it last since you'll have a clearer sense of the project you're summarizing. To learn how to review the current literature on your topic, keep reading! Did this summary help you? Yes No

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How to Write a Rough Draft

Last Updated: February 6, 2023 Fact Checked

This article was co-authored by Michelle Golden, PhD . Michelle Golden is an English teacher in Athens, Georgia. She received her MA in Language Arts Teacher Education in 2008 and received her PhD in English from Georgia State University in 2015. There are 10 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 295,595 times.

Writing a rough draft is an essential part of the writing process, an opportunity to get your initial ideas and thoughts down on paper. It might be difficult to dive right into a rough draft of an essay or a creative piece, such as a novel or a short story. You should start by brainstorming ideas for the draft to get your creative juices flowing and take the time to outline your draft. You will then be better prepared to sit down and write your rough draft.

Brainstorming Ideas for the Draft

• Freewrites often work best if you give yourself a time limit, such as five minutes or ten minutes. You should then try to not take your pen off the page as you write so you are forced to keep writing about the subject or topic for the set period of time.
• For example, if you were writing an essay about the death penalty, you may use the prompt: “What are the possible issues or problems with the death penalty?” and write about it freely for ten minutes.
• Often, freewrites are also a good way to generate content that you can use later in your rough draft. You may surprised at what you realize as you write freely about the topic.

• To use the clustering method, you will place a word that describes your topic or subject in the center of your paper. You will then write keywords and thoughts around the center word. Circle the center word and draw lines away from the center to other keywords and ideas. Then, circle each word as you group them around the central word.
• For example, if you were trying to write a short story around a theme like “anger”, you will write “anger” in the middle of the page. You may then write keywords around “anger”, like “volcano”, “heat”, “my mother”, and “rage”.

• If you are writing a creative piece, you may look for texts written about a certain idea or theme that you want to explore in your own writing. You could look up texts by subject matter and read through several texts to get ideas for your story.
• You might have favorite writers that you return to often for inspiration or search for new writers who are doing interesting things with the topic. You could then borrow elements of the writer’s approach and use it in your own rough draft.
• You can find additional resources and texts online and at your local library. Speak to the reference librarian at your local library for more information on resources and texts.

Outlining Your Draft

• You may use the snowflake method to create the plot outline. In this method, you will write a one line summary of your story, followed by a one paragraph summary, and then character synopses. You will also create a spreadsheet of scenes.
• Alternatively, you can use a plot diagram. In this method, you will have six sections: the set up, the inciting incident, the rising action, the climax, the falling action, and the resolution.
• No matter which option you chose, you should make sure your outline contains at least the inciting incident, the climax, and the resolution. Having these three elements set in your mind will make writing your rough draft much easier.

• Act 1: In Act 1, your protagonist meets the other characters in the story. The central conflict of the story is also revealed. Your protagonist should also have a specific goal that will cause them to make a decision. For example, in Act 1, you may have your main character get bitten by a vampire after a one night stand. She may then go into hiding once she discovers she has become a vampire.
• Act 2: In Act 2, you introduce a complication that makes the central conflict even more of an issue. The complication can also make it more difficult for your protagonist to achieve their goal. For example, in Act 2, you may have your main character realize she has a wedding to go to next week for her best friend, despite the fact she has now become a vampire. The best friend may also call to confirm she is coming, making it more difficult for your protagonist to stay in hiding.
• Act 3: In Act 3, you present a resolution to the central conflict of the story. The resolution may have your protagonist achieve their goal or fail to achieve their goal. For example, in Act 3, you may have your protagonist show up to the wedding and try to pretend to not be a vampire. The best friend may then find out and accept your protagonist anyway. You may end your story by having your protagonist bite the groom, turning him into her vampire lover.

• Section 1: Introduction, including a hook opening line, a thesis statement , and three main discussion points. Most academic essays contain at least three key discussion points.
• Section 2: Body paragraphs, including a discussion of your three main points. You should also have supporting evidence for each main point, from outside sources and your own perspective.
• Section 3: Conclusion, including a summary of your three main points, a restatement of your thesis, and concluding statements or thoughts.

• For example, maybe you are creating a rough draft for a paper on gluten-intolerance. A weak thesis statement for this paper would be, “There are some positives and negatives to gluten, and some people develop gluten-intolerance.” This thesis statement is vague and does not assert an argument for the paper.
• A stronger thesis statement for the paper would be, “Due to the use of GMO wheat in food sold in North America, a rising number of Americans are experiencing gluten-intolerance and gluten-related issues.” This thesis statement is specific and presents an argument that will be discussed in the paper.

• Your professor or teacher may require you to create a bibliography using MLA style or APA style. You will need to organize your sources based on either style.

Writing the Rough Draft

• You may also make sure the room is set to an ideal temperature for sitting down and writing. You may also put on some classical or jazz music in the background to set the scene and bring a snack to your writing area so you have something to munch on as you write.

• You may also write the ending of the essay or story before you write the beginning. Many writing guides advise writing your introductory paragraph last, as you will then be able to create a great introduction based on the piece as a whole.

• You should also try not to read over what you are writing as you get into the flow. Do not examine every word before moving on to the next word or edit as you go. Instead, focus on moving forward with the rough draft and getting your ideas down on the page.

• For example, rather than write, “It was decided by my mother that I would learn violin when I was two,” go for the active voice by placing the subject of the sentence in front of the verb, “My mother decided I would learn violin when I turned two.”
• You should also avoid using the verb “to be” in your writing, as this is often a sign of passive voice. Removing “to be” and focusing on the active voice will ensure your writing is clear and effective.

• You may also review the brainstorming materials you created before you sat down to write, such as your clustering exercise or your freewrite. Reviewing these materials could help to guide you as you write and help you focus on finishing the rough draft.
• You may want to take breaks if you find you are getting writer’s block. Going for a walk, taking a nap, or even doing the dishes can help you focus on something else and give your brain a rest. You can then start writing again with a fresh approach after your break.

• You should also read the rough draft out loud to yourself. Listen for any sentences that sound unclear or confusing. Highlight or underline them so you know they need to be revised. Do not be afraid to revise whole sections or lines of the rough draft. It is a draft, after all, and will only improve with revision.
• You can also read the rough draft out loud to someone else. Be willing to accept feedback and constructive criticism on the draft from the person. Getting a different perspective on your writing will often make it that much better.

Community Q&A

You Might Also Like

• ↑ https://www.umgc.edu/current-students/learning-resources/writing-center/online-guide-to-writing/tutorial/chapter2/ch2-13
• ↑ https://writing.ku.edu/prewriting-strategies
• ↑ https://academicguides.waldenu.edu/writingcenter/writingprocess/outlining
• ↑ http://www.writerswrite.com/screenwriting/cannell/lecture4/
• ↑ https://www.grammarly.com/blog/essay-outline/
• ↑ https://writingcenter.unc.edu/tips-and-tools/thesis-statements/
• ↑ https://writingcenter.unc.edu/tips-and-tools/editing-and-proofreading/
• ↑ https://www.grammarly.com/blog/rough-draft/
• ↑ https://writing.wisc.edu/handbook/style/ccs_activevoice/
• ↑ https://writingcenter.unc.edu/tips-and-tools/revising-drafts/

About This Article

To write a rough draft, don't worry if you make minor mistakes or write sentences that aren't perfect. You can revise them later! Also, try not to read over what you're writing as you go, which will slow you down and mess up your flow. Instead, focus on getting all of your thoughts and ideas down on paper, even if you're not sure you'll keep them in the final draft. If you get stuck, refer to your outline or sources to help you come up with new ideas. For tips on brainstorming and outlining for a rough draft, read on! Did this summary help you? Yes No

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Writing Studio

How do i write a thesis statement.

This page is Part 1 of a two-part handout that continues with our Thesis Statement Checklist .

What is a Thesis Statement?

In an effort to make our handouts more accessible, we have begun converting our PDF handouts to web pages. Download this page as a PDF: See p. 1 of How Do I Write a Thesis Statement Return to Writing Studio Handouts

A thesis statement is a very specific argument that guides your paper. Generally, a thesis statement consists of two parts :

• A clearly identifiable topic or subject matter
• A succinct summary of what you have to say about that topic

For your reader, a thesis functions like the case a lawyer has to make to the judge and jury in a courtroom. An effective thesis statement explains to your reader the case you are going to make and how you are going to make it.

For you as the author, your thesis can also help you to stay focused as a writer and determine what information you do (and don’t) need to include in your analysis.

Traditionally, the thesis statement is found near the end of your introduction , though this may change depending on the assignment and context. Don’t be afraid to draft a thesis statement that is more than one sentence.

A Note on Writing Process

You do not need a perfect thesis statement before you draft the rest of the paper. In fact, you will likely need to modify your thesis once you have a complete draft to make sure that your draft and your thesis match one another. If your argument evolves in productive ways as you write, your thesis should, too.

Honing and tweaking a thesis statement during the revision process is ultimately more important than having it exact and precise during the drafting process.

Characteristics of a WEAK thesis statement

• Vague: Raises an interesting topic or question but doesn’t specify an argument
• Offers plot summary, statement of fact, or obvious truths instead of an argument
• Offers opinion or conjecture rather than an argument (cannot be proven with textual evidence)
• Is too broad or too complex for the length of the paper
• Uses meaningful-sounding words, but doesn’t actually say anything of substance

Disclaimer: This is not a complete list! You can probably think of many more characteristics of a weak thesis statement.

Characteristics of a STRONG thesis statement

• Answers a specific question
• Takes a distinct position on the topic
• Is debatable (a reasonable person could argue an alternative position)
• Appropriately focused for the page length of the assignment
• Allows your reader to anticipate the organization of your argument

Having trouble drafting a thesis? Try filling in the blanks in these template statements:

• In this paper, I argue that _____, because/by _____.
• While critics argue _____, I argue _____, because _____.
• By looking at _____, I argue that _____, which is important because _____.
• The text, _____, defines _____ as _____, in order to argue _____.

Disclaimer: These are only models. They’ll be useful to help you to get started, but you’ll have to do quite a bit of tweaking before your thesis is ready for your paper.

For more on thesis statements, check out part 2: Our Thesis Statement Checklist .

Last revised: 07/15/2008 | Adapted for web delivery: 5/2021

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• Dissertation

How to Write a Dissertation or Thesis Proposal

Published on September 21, 2022 by Tegan George . Revised on July 18, 2023.

When starting your thesis or dissertation process, one of the first requirements is a research proposal or a prospectus. It describes what or who you want to examine, delving into why, when, where, and how you will do so, stemming from your research question and a relevant topic .

The proposal or prospectus stage is crucial for the development of your research. It helps you choose a type of research to pursue, as well as whether to pursue qualitative or quantitative methods and what your research design will look like.

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Table of contents

What should your proposal contain, dissertation question examples, what should your proposal look like, dissertation prospectus examples, other interesting articles, frequently asked questions about proposals.

Prior to jumping into the research for your thesis or dissertation, you first need to develop your research proposal and have it approved by your supervisor. It should outline all of the decisions you have taken about your project, from your dissertation topic to your hypotheses and research objectives .

Depending on your department’s requirements, there may be a defense component involved, where you present your research plan in prospectus format to your committee for their approval.

Your proposal should answer the following questions:

• Why is your research necessary?
• What is already known about your topic?
• Where and when will your research be conducted?
• Who should be studied?
• How can the research best be done?

Ultimately, your proposal should persuade your supervisor or committee that your proposed project is worth pursuing.

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Strong research kicks off with a solid research question , and dissertations are no exception to this.

Dissertation research questions should be:

• Focused on a single problem or issue
• Researchable using primary and/or secondary sources
• Feasible to answer within the timeframe and practical constraints
• Specific enough to answer thoroughly
• Complex enough to develop the answer over the space of a paper or thesis
• Relevant to your field of study and/or society more broadly
• What are the main factors enticing people under 30 in suburban areas to engage in the gig economy?
• Which techniques prove most effective for 1st-grade teachers at local elementary schools in engaging students with special needs?
• Which communication streams are the most effective for getting those aged 18-30 to the polls on Election Day?

An easy rule of thumb is that your proposal will usually resemble a (much) shorter version of your thesis or dissertation. While of course it won’t include the results section , discussion section , or conclusion , it serves as a “mini” version or roadmap for what you eventually seek to write.

Be sure to include:

• A succinct introduction to your topic and problem statement
• A brief literature review situating your topic within existing research
• A basic outline of the research methods you think will best answer your research question
• The perceived implications for future research
• A reference list in the citation style of your choice

The length of your proposal varies quite a bit depending on your discipline and type of work you’re conducting. While a thesis proposal is often only 3-7 pages long, a prospectus for your dissertation is usually much longer, with more detailed analysis. Dissertation proposals can be up to 25-30 pages in length.

Writing a proposal or prospectus can be a challenge, but we’ve compiled some examples for you to get your started.

• Example #1: “Geographic Representations of the Planet Mars, 1867-1907” by Maria Lane
• Example #2: “Individuals and the State in Late Bronze Age Greece: Messenian Perspectives on Mycenaean Society” by Dimitri Nakassis
• Example #3: “Manhood Up in the Air: A Study of Male Flight Attendants, Queerness, and Corporate Capitalism during the Cold War Era” by Phil Tiemeyer

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If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

Research bias

• Survivorship bias
• Self-serving bias
• Availability heuristic
• Halo effect
• Hindsight bias
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• Reinforcement learning
• Supervised vs. unsupervised learning

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The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A thesis or dissertation outline is one of the most critical first steps in your writing process. It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding what kind of research you’d like to undertake.

Generally, an outline contains information on the different sections included in your thesis or dissertation , such as:

• Your anticipated title
• Your abstract
• Your chapters (sometimes subdivided into further topics like literature review , research methods , avenues for future research, etc.)

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A dissertation prospectus or proposal describes what or who you plan to research for your dissertation. It delves into why, when, where, and how you will do your research, as well as helps you choose a type of research to pursue. You should also determine whether you plan to pursue qualitative or quantitative methods and what your research design will look like.

It should outline all of the decisions you have taken about your project, from your dissertation topic to your hypotheses and research objectives , ready to be approved by your supervisor or committee.

Note that some departments require a defense component, where you present your prospectus to your committee orally.

Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement .

However, it should also fulfill criteria in three main areas:

• Researchability
• Feasibility and specificity
• Relevance and originality

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The Ezra Klein Show

Transcript: Ezra Klein Interviews Dario Amodei

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

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

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

[MUSIC PLAYING]

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

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

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

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

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

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

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

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

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

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

Dario Amodei, welcome to the show.

DARIO AMODEI: Thank you for having me.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

DARIO AMODEI: Yes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

EZRA KLEIN: Oh, Jesus Christ.

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

EZRA KLEIN: So that is fast.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

EZRA KLEIN: Right.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

EZRA KLEIN: Possibly first.

DARIO AMODEI: Yeah, right, possibly first.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

EZRA KLEIN: Dario Amodei, thank you very much.

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

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