Jobs, growth, and the AI economy (OpenAI Podcast Ep. 3)

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Transcript

Hello, I'm Andrew Mayne and this is the OpenAI Podcast.

There's a lot of conversation and debate about the future of AI when it comes to labor and work.

To talk about this, my guests are Brad Lightcap, who's the Chief Operating Officer of OpenAI, and Ronnie Chatterji, who is the Chief Economist.

We're going to find out the kind of research OpenAI is doing, the conversations they've been having, and hopefully get a glimpse of where they think the future is headed.

We had a lot of people coming back to us and saying, "Actually, this is, I think, one of the best things that has maybe ever happened to this industry."

AI is a tool that lets people do things that they have no ability to do otherwise.

They have the world's smartest brain at their fingertips to solve hard problems.

So Brad, you're the Chief Operating Officer, you're the Chief Economist.

Explain what your roles are.

My role probably boils down mostly to what we call deployment.

So zooming out OpenAI is a research and deployment company.

And when we think about our mission, what we really think about is not only building AI and doing the research that underpins the building of AI, but how do you actually take it out into the world and have people use it and have it be beneficial for people, have it be safe for people?

How is it used in one country versus another country, one industry versus another industry?

So I spent a lot of time trying to figure that out, which means working with customers, working with partners, spending a lot of time with our users, and just kind of studying what people want from OpenAI and our products, how people actually use the technology, and then as the technology changes, how that pattern of use changes.

It seems like because OpenAI started primarily as a research org and wasn't sure if they were going to do product or even put things that were sort of public facing.

And so how much has this changed rapidly for you?

It's changed really quickly.

I think chat GPT in November 22 was kind of the pivotal moment.

And it was the first time that we really saw AI used at scale.

And I think what we kind of, and it's interesting almost the story of how we actually learned that and how we made the decision.

And how we made the decision to do chat GPT, which was we had previously built an API for developers.

And we had a thing, you'll remember, Andrea and our API, that was the playground, where you could basically try prompts out and see how the model would complete the prompt.

And this was back in the days of like the models just being purely completions based, where they take an input and they kind of continue the text on predicting the next word and the next token in the sequence.

And people were trying to like hack the playground to figure out how to get it to talk to them.

And they almost, you could tell people kind of wanted this conversational interface.

And so we kind of learned from that and we built chat GPT as the first version of a conversational interface, where we taught the model how to instruction follow to be more responsive to what people wanted to talk about.

And that very much surprised us and became, I think, the kind of dominant paradigm of what we call the first era of AI, which was these kind of chat bots that really were good enough to be engaging for people and be helpful for people.

Yeah, it seems like, because at the time, we kept thinking that like GPT 4 would be finally when it was really useful and chat GPT was built on top of GPT 3.5.

And it seemed like certainly changing the interface was helpful, but we thought we needed a faster, smarter model.

But it was actually the interface was such a big unlock.

And that was, I had the problem whenever I would do demos at GPT 3, it would be this blank canvas.

I go, now you do something.

And people be like, I don't know what to do.

But once you put it into the chat interface, they go, oh, well, I'll ask it a question.

I'll ask it if we do this.

And that was such a big unlock.

But then the pace after that, like you said, was insane because chat GPT exceeded beyond any expectation here.

I think there was an expectation it would kind of level off, but it didn't.

And then pretty soon, there was this awareness.

I think people thought AI was something in the future.

And now it came into the present and now bringing in an economist to come help map this out and figure this out.

So what is your role?

As you say, I mean, the future has arrived more quickly than any of us could have imagined.

I joined at a time when we were deploying intelligence at scale into the economy and society.

And my job is to help people understand what the impacts of that are going to be on businesses, their jobs, their relationships, the way government does policy, and develop forecasts to help people understand how to make investments with their time and overall with their resources.

And so as an economist, it's an amazing time to join because I think we're at a beginning of a real transformation in the economy.

And it's something that I think people need to be prepared for.

So the biggest job I have at OpenAI is developing indicators to kind of tell us where the economy is going and communicating that to people all over the world, because this is going to be bigger than just the United States and what we do here.

But something that's actually going to transform people's lives around the world.

Okay.

So in my experience and limited experience of understanding when corporations employ economists, often it's to figure out, let's say, the prices of products or things like that to make kind of predictions.

But here, your job isn't just internal, so it's external.

So how are you sharing this?

What is OpenAI doing to help people understand where things are headed or what we think they are?

You're right.

I mean, there's a tradition of economists joining companies and in tech specifically.

This job was designed a little bit differently, and I think it reflects that this company really has research roots.

And I think people really wanted to be a job that, yes, thought about pricing and A/B tests and analyzing data from the platform.

But maybe more importantly, also thought about how is this going to change the world and doing rigorous research, just as rigorous, but in a different way as our AI researchers do, in terms of what's going to happen and how can we tell people about it.

How do we get people ready for this?

And so a big part of my job is external.

You know, since I started, I've been in London and Brussels and Delhi and Washington will eventually go to Sacramento and Sydney and every place in between.

But it's so interesting to see the conversation and the vibes across those different markets and how people are thinking about this and the different use cases.

So I have to say, as much as we go out and do that work, I learn as much in those interactions as I probably teach.

But a big part of my job is external and getting sort of people ready for what's happening right now.

Well, there's a lot of anxiety because I think open AI was caught off guard by the success of chat, GPT and the rate of adoption in the place that's being used.

And I think every technology, you know, every disruptive technology, people, there's a fear of change and change is inevitable.

But there is the fear of how it's going to change work, you know, how much it's going to change labor and employment.

And how much does open I think about that and how much of what you do is sort of like thinking about helping people adapt to that, et cetera.

Yeah, I mean, I would say it's it's something that we we look at a lot.

I think Ronnie probably looks at it through one lens.

I kind of somewhat look at it through the lens of what are the things that we need to build to accelerate the opportunity that I has to be impactful in a kind of an economic and outcome oriented context.

And that could be at a micro level.

It could be an individual person, for example, trying to better understand their medical care.

It could be at a macro level at a firm level.

It could be a company that's trying to think about how to accelerate software engineering and pull forward projects from next year into this year.

So all of these things actually, I mean, you know, Ronnie probably does the interesting kind of studies on these things and takes a much more scientific vantage point.

We take a very product advantage point on it, which is how do we actually go build the tools that are representative of the things that people actually want from the company?

People actually want from the systems.

And so, you know, software engineering is the is the thing that I think right now is super interesting is the systems we're building are progressing at just an insane rate in terms of their capabilities in software engineering.

You've seen rise of tools like cursor and windsurf and others.

And we think there's a huge opportunity there to help software engineers and kind of entirely change the tool set of software engineers to make them not 10 times 10 percent more productive, but maybe five or 10 times more productive.

And then Ronnie gets to study the impact of that, you know, on an economic level.

It's amazing.

I think about exactly this way.

It's almost a handoff from what Brad is leading on the product side.

OK, now our software engineers have these amazing tools, intelligence at their fingertips to be more productive.

You know, we might across the world write like a few billion lines of code in a day.

And now you could multiply that by 10 X.

What could we build?

What could they build if you can write that much more code and that much sort of even better code potentially than you could on your own?

That to me is a huge economic opportunity.

And so, yeah, my job is to pick it up from that angle, understand how a software engineer's job is changing, how she might be using these tools to do things she couldn't do before and how the organization that she works in is also going to benefit from that, creating more productivity and ultimately value for the economy.

So I see a super interesting challenge.

The other thing I'd say is like scientific research is one I get really excited about.

So I think taking Brad's analogy, it's like we want to put amazing intelligence in the hands of scientific researchers.

Why does that matter?

Because, you know, science drives growth, it drives economic growth.

And so we can accelerate science, accelerate discovery.

We're going to have more economic growth and more good things for everybody.

And so I always think about if I can study how science is changing with our with the use of our products and it'll be a useful contribution in terms of economics, but also just the world.

Yeah, I want to touch on that a second.

But what in the software space, I think you've seen there's been I've seen a lot of people have a concern because all of a sudden companies are saying, oh, we don't need as many developers now.

But I would say the broader picture is we're never going to be done writing software.

There is always going to be more need for software than there is right now.

And I think the challenge is that some of the bigger companies are getting a bit disrupted or internally.

But we need to think about where the smaller companies, the more agile ones are going to come in and where they're going to come from, because I think small teams can do a lot more.

And has that been some people observed where, you know, let's say some companies are saying, OK, we can do more with this tool.

And what we're seeing smaller companies come forth with new solutions.

Yeah, for sure.

And I think that is the trend line of A.I. Fundamentally is the world is rate limited by talent, by people, real economic growth in the world.

And in the world rounds to zero in most places.

And why is that?

Right.

It's because it's really hard for the average company, whether it's a small business, large business, financial services company, an insurance broker, a hospital to find people that can actually produce better tools, better systems and ultimately better outcomes for customers.

And if you go ask kind of any company, you ask any company in Silicon Valley, you know, where if they need to hire more engineers, the answer is almost always yes.

And this is this is the mecca of engineering of software engineering.

Now, imagine what the rest of the world looks like.

And so just taking software engineering as an example, we see it as not only incredibly, you know, there being an incredible opportunity to inflect outcomes for those companies, you know, for companies large and small.

But we see it really as incumbent on open A.I. to be able to build the tool sets, the models, all the safeguards, all of the compliance schemas and all that to be able to actually serve these tools in the places that they need to be.

And it's interesting kind of the polarity of it.

I think, you know, on the one hand, you've got this tool set that is going to be incredibly enabling of people who have no sophistication on the subject matter.

So you've got companies now building tools that are enabling people to build software who've never written a code line of code in their life.

And on the other hand, you've got these tools that are incredibly sophisticated and taking, you know, level 10 engineers and making them 50 percent to export productive.

And it's a remarkable thing that you can get both of those effects.

Something that was interesting was using the modern case example where they deployed chat chip enterprise.

And one of the things that happened internally was we had people developing their own GPTs.

And sometimes people go, what's happened to GPTs externally?

But I think that's been interesting thing is internally, somebody who may not have thought about how to build an agent or something like that, who may not be technically inclined is able to do that.

And has that been a common trend with other companies that are now just building on top of the platform?

Yeah, I mean, I think that is fundamentally kind of how this is going to work.

I think A.I. at its core and its essence is a tool that kind of lets people do things that they had no business or ability to do otherwise.

And there's going to be kind of crazy outcomes that come from that.

I think it's kind of somewhat unpredictable.

And if you kind of look in the long arc of history of what makes for these kind of disruptive platform shifts, to me, the thing that is kind of demarcating of that is when you now have people who actually have the capability to go off and do something at either a much higher level of productivity or something that's parallel to the core thing they're doing that they couldn't do before, where they were kind of rate limited or gated on someone else being able to having to do that thing for them.

And so that's GPTs are a good example of how you now have someone who can configure what could be a fairly complex workflow.

And it's on us to continue to build a product that enables even more complex workflows over time as the models get really good.

And that's a remarkable thing.

What sectors do you see being impacted next?

I think that we're just scratching the surface when it comes to scientific research, areas like drug discovery, material sciences.

I think the next couple of years, you're going to see massive discoveries in those spaces for the reasons that Brad is talking about.

When I think about science, I think about an endless corridor with doors on either side.

And scientists, researchers in companies have to make decisions about where they're going to explore.

And that's a rate limiting sort of situation to Brad's point.

You can't explore every door.

But what our tools can help you do is actually look behind all those doors and take a peek and figure out where you want to spend the time working on the hardest problems.

And I think if we can accelerate science in that way, you're going to see massive discoveries coming out of private sector labs, national laboratories like many of the ones we're already working with, and the public sector.

And so I expect those areas in research to really be transformed over the next several years.

I think you'll see a lot of different discoveries that we wouldn't have thought possible happening more quickly.

I think another area is going to be on sort of professional services.

We both work a lot.

I know a lot of folks who are in this industry, either whether it's private equity, investment banking, consulting, so much of the work there that people are doing.

We can augment that work by -- I think about the way I use our tools to create slide decks or prepare for a presentation.

I can now focus on the higher value and higher margin things that are important for my job now that I can use our tools to do some of these things that I was going to have to do myself.

And so I see professional services as a key area where a lot of consultants, bankers, and private equity executives are going to be able to use this in a big way.

So those are two areas I see finance and science-driven discovery companies being really revolutionized by our tools.

And I would say it's not just the -- on the science side at least -- it's not just the depth of any individual step of the work.

So certainly you can now do this more multifaceted exploration for any given thing.

But it's the breadth across the span of the work that these models can reason over.

So having these systems able to understand -- if you look at kind of how a drug gets developed, for example, there's some number of insanely complex, discrete steps in that process that all require kind of handoff at various points to a lot of different people who all have to kind of gather context from the person before them and kind of prepare context for the person that comes after them.

And you can actually schematically break it down.

And to have models basically woven across that entire workflow, not only are you enabling the scientists to go deeper, but you're actually enabling the people who work with and around the scientists to actually kind of accelerate the end product, you know, ultimately to a better outcome and ultimately faster.

One of the limitations I've seen -- so one of the companies I've worked with, they're doing drug discovery.

And the models are great at suggesting things, but it still comes down to the clinical trial and the lab bench and things like that.

And hopefully we'll find ways to accelerate that.

But what are some of the other limitations, either what these things can do or bottlenecks for us seeing sort of the benefits?

I think human judgment decision making is going to be really important.

I actually think it might be more important.

You know, what we're finding in a lot of research, and one of my colleagues in this is David Deming at Harvard, he has this research that shows that people who are great at leading teams, let's say someone like Brad at the top of the company, they're also the same people who are great leading agents.

And I think that a lot of the skills that let people make great judgments lead teams.

They're going to be even more important and at a higher premium in this economy.

And so I feel a situation like this where firms are using drug discovery, you're still going to need the judgment of experts.

You're going to need refinements on the experiments and you're going to need help in terms of scaling.

I also think there's other institutional changes, though, that might accelerate science.

Clinical trials come from an old world of how we used to test drugs for safety and efficacy.

Those are really important.

But everything from the sample sizes to how you enroll people, I mean, our tools could be hugely helpful in those areas.

So I feel like you're going to see it in drug discovery, but you're also going to see it in every part of the value chain for, let's say, a pharma biotech company that might ultimately not just increase the rate of discovery, but the rate of commercialization and scale.

That's my hope.

You just mentioned agents.

And I think it's a word that's kind of like the word of the year.

People hear it and sort of there's all sorts of definitions of it.

Do you want to take a stab at that and kind of see how you guys see that playing out?

I mean, I'll probably get yelled at by someone.

No, it would not be controversial at all.

Everybody will agree.

I mean, for me, agents, I have a very high bar.

It has to be a system that can be reliably handed, complex work that it can take on autonomously and execute at a high level of proficiency where it hasn't seen that work before.

And that last part is a critical piece is these aren't just things that are trained to copy.

They have to be things that kind of implicitly leverage the reasoning ability of the model to solve new problems.

And this is going to be important in a lot of domains.

And so people use the word agent.

I think there's maybe an enterprise productivity context of it.

There's maybe a science kind of context of it.

There's a software engineering context of it.

But the kind of common thread for me is it has to be something that you can actually hand something to you almost work in tandem with kind of like a teammate.

And that teammate could be a scientist.

It could be a software engineer.

It could be a data scientist.

Could you give me a hypothetical example of like a kind of task?

Yeah, I mean, I think software engineering has an obvious set, which is you could ask it to basically go off and actually write code for you.

And then kind of similarly go do the QA, go do all the unit testing, go automate kind of meaningful parts of this process of code writing.

And in different contexts, I would say it's working with agents that can make your sales teams more efficient.

So slotting into parts of your sales funnel where you have a volume problem, where it's like, OK, I've got 100,000 inbound leads for a thing.

But I've got five people to look at them.

Can you actually have an agent that can ingest those leads and understand those leads, process them, qualify them, move them through your funnel, recommend who should talk to who, recommend all the follow up steps and ultimately kind of drive a lead toward a conversion.

So it's a generalizable concept that kind of maps in any number of areas.

Do you see this like where I might email an agent or something and say, OK, I need to just treat it like I would another employee?

Yeah, I think that's kind of the interesting part of it is that in some sense, there's the kind of the input mechanisms will be specific, I think, to the user.

Right.

If you are a software engineer, you may want that agent living in your IDE.

If you're a scientist, you may want it living in the software you use that you do experiment design and execution with.

If you are doing user operations or customer support, you may want it sitting in your inbox because that's where your work happens.

And so how do you build product that is intelligence underneath but is extensible into kind of any number of surfaces and can be without compromising the reliability and the power of the system?

It's actually a hard product problem.

I have friends that are pretty chat- GPT focused power users and have heard comments from before of wanting to sort of do more with it and even small business owners, too.

The idea that if they could have like a virtual chat-GPT agent or something like that, is that something you see in a near term horizon that I'd be able to get to take care of a lot of the little work that there's just not enough hands to do?

I think it's a really amazing near term application in my view.

You know, you think about the limits around the world of growing the economy.

One of the biggest ones is small business.

There's what they call in economics a missing middle in so many countries where you had a bunch of small businesses and you have a few large businesses, but the small businesses don't grow large.

And it's a big benefit of the US economy that our small businesses and entrepreneurs can actually grow and scale.

In most places around the world, that's not true.

Why is that not true?

Because they often don't have the mentorship, the coaching, the support, the advice to actually know what to do to grow their business.

Now, imagine you democratize an AI agent that understands the basics of how to grow a restaurant business or an e-commerce business.

And that's relatively easy to do in terms of instantiating that kind of intelligence into an agent.

And then a small business owner could leverage that advice and decide, "Oh, maybe I should change a menu item or hire a sales rep or do something different with my strategy that could help them grow."

And I think for small business owners around the world, including the United States, tremendous opportunity to get small business advice, evidence-based advice from agents.

That's something I'm very interested in and I know a bunch of folks around the world are working on.

So I want to address that when the evidence-based approach in a second.

But tell us more about what you're seeing from developing economies.

Because I know that's a big area of concern.

One of the fears is that there's a lot of lower-level knowledge work that's done in developing economies.

And the fear is that AI is going to take that away.

But you just brought up the fact that there are these limiters there that all of a sudden get unlocked.

I think there's a lot of opportunities we should be talking about as well.

I know that when I work in emerging markets, there's a lot of human scaling problems.

It's related to what the rate-lovering factors that Brad talked about with Silicon Valley hiring engineers.

One of the biggest returns on investment in Africa is agricultural extension support.

What that means is helping a farmer figure out what kinds of seeds he should be using, what kind of fertilizer he should be using, what kind of farming techniques he should do to get the most out of his land.

Because a lot of people are small-scale subsistence farmers.

If we can increase productivity for that farmer 10, 20, 30%, it is life-changing.

And we have people who are trained up to do that, but there's not enough of them.

And when these extension support services are offered, there's always someone, probably 10, who don't get the service for every one person who does.

Now imagine that we could have intelligence provided to those 10 who never got that service to begin with.

I think when you think about agricultural extension support scaling with our tools, it's a huge opportunity to improve lives of people in lower-income countries in emerging markets, particularly in agriculture.

I'd say the small business one is another example.

We know from the United States, one of the best ways to move up the income and wealth ladder is start a business.

That should be true in other places, too.

But there's so many limits to scaling, and often it is hiring the right person or getting the right advice.

Those are two opportunities, I think, if we can do this right, are going to make a huge impact for the positive in those parts of the world.

My mother-in-law is in India, and she has a candy company.

She uses chat GBTO a lot to help her plan menus and recipes and write stuff.

It's been an interesting unlock because now I've seen, I think, Sherry did, had quality before, but now it let her basically spend more time on other things.

It's interesting because you've seen an African development where cellular was a bigger change than anybody predicted.

You took a country like Kenya, which maybe 5% of the population had phones, and it was all controlled by the government or something.

Then when cellular came through, then you had people able to figure out how to go to market, you had all sorts of commerce stuff, things.

What changes are you seeing right now with chat GBTO-like technologies?

To your point, first, if you're one of the most running Indian sweet company, I've got three little interns in my household who'd love to point, so just let us know if there's a job opening.

This is where the disruption is both exciting, and I also understand it induces anxiety, but you're exactly right.

When you look at the Kenyan experiment, when they leapfrogged a generation of technology when new innovations came out, we're now doing something fairly radical, which is putting intelligence in individuals' hands.

When they have a chat GBT account or subscription, they have the world's smartest brain at their fingertips to solve hard problems.

It's not intermediated by a government or a big business.

It's something they can use to solve problems.

I'm really optimistic about the problems people are going to choose to solve.

One of the coolest things about this organization is we don't really tell you what problems to solve.

That was one of the most interesting things I think here.

When you think about how people are using chat GBT, it's a wide, diverse set of uses, much less how they're building on the API with our developers.

People will choose to solve the problems that are most relevant to them.

That's going to be incredibly transformed for their lives, but also disruptive, because they're going to be able to have that power that they didn't have before.

When I think about it as an economist, those are the kinds of transitions I want to study.

I want to understand.

I want to make it easier for individuals, organizations, and society.

I think the level that you're talking about happened in Kenya and other parts of the world.

This is a much bigger transition than we're on the verge of.

It's something that my team spends a lot of time thinking about when we look at data, not just looking at the US and Europe, but looking at other parts of the world.

You mentioned before, in working with agents, how having managerial skills with the ability to delegate is important.

Could you expand on that?

Also, maybe what other skills might be important that people need to be thinking about that they want to develop?

AI is interesting because it really is a reflection of your will and your desire.

I think the sky's the limit in terms of what it can do for you.

If you wake up one day and you decide you want to start a business, that just got meaningfully easier.

If you wake up one day and you decide you want to build a piece of software, that got meaningfully easier.

There's an incredible level of agency that's required to extract the most out of AI.

As we think about where the product moves, our job is to try and lower the bar so that you can simplify the path from idea in your brain to outcome.

There are interesting ways in a meta-sense that models can actually help do that.

I think that's probably, to me, the really important thing.

Agency is going to matter a lot.

You're going to see the rewards accrue to people who are, as Sam said it the other day, is the return of the idea guy in some sense.

It's the people that I think can not only figure out what it is that they want and what good looks like, but then can figure out how to activate the systems to be able to work on their behalf.

There's going to be people that do that incredibly well.

One of my personal bars for how impactful our work ends up being is, will you see the rise of companies that are 1, 2, 5, 10 people that are doing a billion dollars in revenue?

That's the ultimate agency outcome.

If you think about it, it's like you have a very small set of people capable of commanding what could be this very large-scale enterprise, mostly because they are opinionated about things like sales, marketing, products, software engineering, and so on.

I think that's going to be a really cool thing to see.

Mark Bittinghoff had said something along the lines that they weren't going to be hiring any more software engineers, which maybe they overhired too, I don't know, but then that they're going to be increasing the number of sales people.

I think that often people hear the word sales and they think somebody calls you up randomly or cold calls, but sales is actually a big part of it, as people who are networked, who know a lot of other people.

I think that's what he was talking about, was what was going to be really valuable to the growth.

We're humans with human connections.

Is this something you've seen data to back this up or to see this as a high growth area?

A lot of the research coming out on this is showing that EQ matters a lot.

A lot of people think in this world it's getting more and more technologically sophisticated.

All of a sudden, the soft skills, the social skills being connected with people would be less valuable.

It's actually the opposite.

Once you make these abilities and these capabilities democratized, to be able to write code, for example, then some of the other things actually start to matter more in the market.

I'm not surprised at all that salespeople who have deep technical knowledge, and we have many here in Brad's work and across the organization, are going to be at a premium around the world.

Those are people going to be able to connect the dots, use their EQ plus their technical expertise to solve problems.

I feel like when you're thinking about what skills we want in the economy, that's going to be a key part of it, as well as critical thinking and decision making.

We're still going to need people to identify those problems to chase after.

That's where Brad talked about the agency combined with the ability to target the right problem is going to be at such a premium.

I expect that to be really important.

I've seen in tech, I think there's this over-indexing on IQ and horsepower.

I'm a big believer that I think these systems are going to be able to do just about any cognitive tasks we can think about.

But you brought up EQ.

We think it's a really important one.

Enough attention gets paid to that because I know some small companies that scaled really big and they built great products.

I can't get anybody on the phone.

I can't talk to anybody because they're just focusing purely on the technical component and not where they exist in the network of people and everything else like that.

What are ways that somebody right now who wants to find themselves in a very aligned position with the future, how do they build these skills?

How do they work towards that?

How do organizations find people or foster that?

I think it starts in schools.

One of the really exciting things about the moment we're at is education is going to change.

I know that also creates a lot of excitement and anxiety, but I think so many things that we're learning in school, I have younger kids and so in elementary school grades, they're going to be even more relevant.

What are you teaching people when they come into pre-K or kindergarten?

You're teaching them how to be a human.

That's going to be a better set of skills to learn now and how to be a human because that's going to be how you become a better complement for this amazing intelligence.

As an economist, you think about two constructs, substitution, which creates a lot of the anxiety, but also compliments.

If humans can become a complement to intelligence and leverage it with agency, that is going to be the unlock.

I feel like a lot of schooling in the early stages even now, and it'll be more so as we go forward, is teaching those soft skills and how to be a human.

Later on, critical thinking, financial numeracy with numbers, still going to be really important.

My kids have calculators, but I still want to teach them how to do multiplication tables.

Dictation software works really well.

I still teach them how to write.

You'll need those skills and you'll need a sense of some other kind of higher order cognitive skills, resilience, grit, things that they're going to need to adjust to these changes in the market.

When a CEO says, "Look, we're looking for more of something like this instead of that," students in the future are going to be able to prepare to pivot in the right way and have that baseline skill.

That's kind of how I think about people preparing.

I think education will play a big role.

I think work experience at great organizations can play a role too.

Those are the two areas.

I've been advising some students, and I want to name the college, but it's in the Bay Area.

It's a pretty good college.

They have a pretty good CS, Computer Science Program.

Do you know how many days they spent in the last semester learning how to use tools like Winsor for a cursor?

I don't know.

Tell us.

Zero.

Zero.

None.

Other professors have taught them anything about how to use AI coding agency.

They're really using it in the background.

Yeah, they are.

I'm also the ones that aren't.

I strongly encourage them to that.

I think that was, for me, a surprising thing to find out that at that level, they're about to be put out in the workforce.

They're not even getting a day.

I understand.

You want them to understand the fundamentals.

You want them to understand that, but they're going to be applying for jobs.

I helped them put together projects and stuff so they can get jobs from places.

What is OpenAC?

Its role in policy, both from education and policymakers and stuff and trying to advise or influence?

It's a good question.

I think there's no question that we're headed toward an overhaul, I think, of how the education system works.

I think that will be a positive overhaul.

I mean, at the most reduced level, what is it that we're building?

You've got this thing now that is this personal tutor of every person on Earth.

As it gets better, it will start to understand you better.

It will understand your rate of learning better.

It will understand how you like consuming information.

Are you more visual?

Are you more quantitative?

Do you need things explained in certain ways?

We've had the amount of feedback we get from people, for example, even with children who are dyslexic trying to learn the impediment that that creates in the learning process and ways that AI can unblock learning for that population.

It's consistent.

I think that the entire way that we think about education and what education is in the country will have to adapt.

I think it'll be good, though.

I think it will force, in some ways, our systems to think about what are the ways that people will use these tools in the future.

I think the example you gave is in some ways surprising, but in some ways not.

I think the people adapt faster than the institutions.

The question here will be, how do we work with policymakers and with the institutions themselves to try and help the institutions adapt?

I think the ones that do, though, will have this incredible accelerant.

I think that you will see the outcomes among students and the ways that they think about what this tool can do in the classroom will just fundamentally change for the better.

It will also then free up teachers, free up students to spend more time on things that are going to be the high-leverage skills of the future that Ronnie mentioned.

Things like decision-making, things like critical thinking, tool-based problem solving, how do you develop agency and conviction early in children?

I think that that type of thing is going to be super important as opposed to a curriculum that today reinforces things like memorization, regurgitation, and so on.

I'm pretty optimistic that we can make these changes in the education system.

I think it's going to come from teachers and students, the way Brad's talking about.

In the early '60s, President Kennedy said we were going to put a man on the moon.

If you look at what we actually had in terms of national assets at that time and the scientific capabilities, that was a pretty far-off goal.

During that decade, we dramatically increased the number of people doing PhDs in science and engineering as people geared up for this challenge.

I do think there's a really strong role for leadership across sectors to sound the clarion call and say, "Look, this is where we're going to go."

When I think about open AI, I think about we have the best information about where the technology is going.

That's an important role to play.

To let people understand, "Here's what we're building."

Other people in society, education leaders, government leaders, business leaders in other sectors will be able to see it from their perspective.

But if we put that call out there, I think you're going to see a lot of dynamic changes across.

Brad and I are both loyal dukies, of course.

At Duke, I expect the curriculum in computer science and economics to be really different five years from now in a lot of positive ways.

I expect a lot of experimentation beyond whether you can use chat GBT to study or how you regulate in the classroom as a professor.

Really important points.

I don't want to downplay that.

But more important is, how are you going to use this stuff to do stuff, topics in your curriculum, help students who maybe can't learn from a graph but can learn from an oral presentation or teach students the same thing, though in three different ways so everyone in the class gets it?

There's so much amazing stuff that happened.

I do think it'll happen.

I think we have a history here in the United States, and you'll see this around the world as well.

But I know the US is the best, where we've actually responded pretty dynamically to some of these big challenges.

Can you talk a bit about open AI's engagement with educators and policymakers specifically about what you are doing?

I can start with the example of Cal State University.

For those of you from California who spent a lot of time here, Cal State is just the ultimate unlock for students who are first generation, whose parents maybe come from another country or they haven't attended higher education.

Those are the kids that Cal State specializes in.

Those are the students that Cal State has traditionally, for its long illustrious history, taken on the next level.

We're proud to work with them.

This is something that comes from what Brad is talking about, the research and the deployment.

Someone like me picks it up and says, "Okay, now that we're working with this great institution, how are we going to maximize the outcomes for students when they go for that first interview?

Can we prepare them with the skills they need to do well?

Can we track their career outcomes over time and say, "You know what?

Having access to this intelligence made a huge difference."

That engagement has led me to work with administrators at CSU, researchers.

Once we get everything in order, students ultimately to make sure we're going to make a big difference.

For me, it's been a great interaction and it's been facilitated by the deployment we've done with CSU.

That's an education example.

Education has been, for us, the fastest growing segment that uses chat GPT and other open AI tools.

It surprised us a little bit, I think, in some ways.

We knew early on when we launched chat GPT that it had a resonance with students.

It was clearly applicable to the way that people wanted to learn, engage with information, engage with knowledge, test their own learning abilities and skill set.

Funny side story is, right after we launched chat GPT, we launched it in November of '22, we had basically the remainder of that school year where I think there was a lot of upheaval, I would say, in that sector.

You probably remember this.

For a while, we all looked at each other here and were like, "Man, I don't know what this is going to ultimately lead to for us.

Is all this stuff ultimately going to get banned?"

Something over the summer of '23, as the school year changed over, I don't know what it was that went around, but when everyone came back in the fall, the level of enthusiasm and I think the level of forward-lookingness of the leadership in the broader American educational system had changed.

We had a lot of people coming back to us and saying, "Actually, this is, I think, one of the best things that has maybe ever happened to this industry.

It's meaningfully changed how my students are learning.

We're starting to develop perspective on how people are really using this.

Not only do we have that perspective, I actually want to extend and develop that perspective so that I can figure out how to better use this in my classroom, work it into my curriculum, challenge students in new ways, figure out ways that we can actually have it surface gaps and vulnerabilities in certain student populations that maybe aren't getting the attention they need.

All that work now is culminated in work that we're doing internally with an EDU team here at OpenAI to try and work more with the sector.

Ronnie mentioned the Cal State.

Example is just one of many examples of ways that we're trying to engage.

Part of it is product building.

Part of it is engagement.

Part of it is policy.

But we are going to take a whole-of-company approach to it.

I remember one day in the school system, but they famously had banned it.

They're like, "Oh, we're banning this to use the school system."

Then I'd heard anecdotally that a number of teachers within had been using it and having really positive outcomes from any of the reasons you pointed out.

I helped do a study when I was here, and one of the number one feedback we got from students was, "It doesn't judge you.

Chatcheapie doesn't judge you."

It was a great way if you're feeling you're going behind or whatever to go ask questions and get up to speed.

Then we saw that some of the teachers were getting really good results in the classroom and then went to the school system and said, "Listen, no, we need this.

This is something we've been sorely lacking."

There was a famous reversal on that.

That happened faster than I expected.

Would you say that you're seeing probably a faster adoption than you'd been expecting or was I just not with it?

I've been seeing it.

I think you're right.

There was that transformation sometime in 2023 where people realized, "Wow, we can unlock a lot of value here for students and for professors."

Maybe what happened over that summer, I don't know, maybe it happened for me.

One of the biggest barriers to innovation for new faculty members, let's say, at a university, is developing new curriculum.

Someone says, "Look, hey, this topic's hot.

Why don't you develop a whole class on it?"

Professors want to help their students.

They want to introduce them to the new material.

But there's a huge cost as it puts together against your research, your other teacher responsibilities.

All of a sudden, I can use the tools in chat GBT to develop that syllabus.

I can make a great entrepreneurship in AI syllabus now much more quickly than I could before.

It can help me decide what classes I'm going to teach, the slides I might use, the readings I might assign, even discussion questions for my students.

When you lower the barriers to creating new content, it becomes even more exciting for a professor to try something new or a teacher in the K-12 context.

I feel, actually, that now as faculty and teachers are unlocking that, you're seeing a lot more adoption.

I think the other thing is that at the end of the day, introducing students to new ideas they wouldn't have had otherwise is such an amazing thing.

Any teacher sees that and there's a spark.

That's going to make them want to find a way to use those tools.

We definitely need rules and policies they'll set up at the school level.

That's really important.

When and how students use these tools, that's going to be key.

I imagine those will be worked out and there'll be variation across different educational institutions.

But I have no doubt that it's going to be a huge part of education given how valuable it is.

We talked about this a little bit before we started recording, which is there has been years and years, a century of speculation of what happens when you have intelligent systems, how is that going to disrupt the world, whatever.

Now we're in the place where we're actually starting to see this happen.

We realized that I think a lot of it was fanfic and it was just scenarios.

The scenario is playing out and it's very different.

I think your approach has been, you're very evidence-based.

It's the idea that you prefer research over theory.

Where are you directing your research right now for impact and guidance for policy?

For my work, at least on the economic research part, that narrow piece of it, I've been thinking about a couple of things.

One is which sectors are going to be affected first.

I think what I can do to help the organization, but also the world, is if I can identify that sectors like healthcare and education might be transformed more quickly, let's say, than retail and finance, that's a really important insight to provide to the world.

If people are in those sectors and thinking about their jobs and what they can do, it both unlocks opportunity on the enterprise side, but also helps people plan their careers and make their investments.

One of my big goals is to figure out which sectors are going to be influenced first and by how much.

The next thing I've been thinking a lot about is which countries, which geographies are going to be most affected.

I don't think that's really helpful.

When I look at previous technological transformations where people were left behind, a lot of the impacts were geographically concentrated, let's say, in big manufacturing hubs in the upper Midwest and the United States during the last transition.

When you look at that disruption and the scarring that happened over many decades afterwards, I realize that if we can develop good indicators of where, in terms of geography, these effects are going to be most pronounced, that's going to be really, really helpful.

My team spends a lot of time on that as well.

The last piece is communicating it.

A lot of economists, or if I was in academia, that's sort of the last piece.

The piece you tack on there and say, "Okay, well, somebody besides my mother is going to read my paper with its 33 appendices."

In this job, especially given the privilege that I have to be close to the researchers who are changing the world, I've got to be able to translate that for real people.

Those are the three aspects, I think, where geographically, which industries, and explain to the world how that's coming.

That's kind of where the evidence base that I at least want to develop is coming from.

It seems like a big unlock that kind of went unnoticed was when chat GPT went to, you had to have a credit card, you had to have your login and all that, to now you just go to openingi.com and you can just use it, which just increased accessibility around the world.

I think it's been iPhones now, and seeing that kind of rule out there, which I think was a really good democratization of it, was the idea that it went from only a certain part of the world was going to have access to it to now anybody in unrestricted countries, you're able to use that, which I think is great.

I think that's very cool.

You mentioned, though, research into sectors are going to be effective.

What have you found out so far?

So far, I think the sectors that are less regulated, where there's rest, let's say, sort of red tape, rules of the road that need to be followed, those are the sections that are going to change the quickest.

Sometimes that's like healthcare for very good reasons.

We have HIPAA, Protecting Patient Privacy, we have rules on how care is delivered.

These are really important parts of the U.S. healthcare system, and they are similar around the world.

Those sectors are going to be harder to change, and they're going to be slower to adopt new technological tools.

And that's not just true for AI, it's true for previous incarnations of technology.

IT moves slower into healthcare and education than it did to other sectors.

So I think where you have sort of high levels of regulation and compliance requirements, you'll see slower adoptions and those jobs changing slower as a result.

It doesn't mean we can't unlock a lot of productivity in healthcare delivery and education.

In education, we're seeing this on the student side, in teachers, but overall implementation, I think, you'll see it move faster in sectors where the regulations are as sort of significant as you are in those two sectors.

That's key.

And the other thing you'll see is where the workforce is going to embrace it.

Brad made a good point earlier.

It's like, this happened with enterprise software.

People brought tools to work, like new storage solutions, and then their CTO was like, "Hey, what are you doing there?"

And then eventually they're like, "Wait, this thing you're bringing is actually-- this company should adopt it."

In sectors where you have highly skilled workers who are bringing these tools to work, using things like chat, GPT, building on our API, those sectors are going to transform more quickly.

And that's why I think places like finance, sort of research, drug discovery type organizations, that's places where I think you're going to have those people bringing it to work to solve problems.

I expect those sectors to move pretty fast.

What career advice are you giving your children?

That's the hardest question.

And what I tell my kids is, when I was growing up, I was a son of immigrants, right?

So if your parents are from a certain part of the world, the advice you might get would be, there's only two choices, right?

There's like, be a doctor or be an engineer.

And if you're really creative, you can be a biomedical engineer.

So there's like a narrow set of choices.

Why would parents give those advice to kids?

It's because it's like they would predict, these are going to be the stable professions.

But during the course of that generation, healthcare changed a ton, right?

We had managed care, a lot of physicians worked for hospitals.

The job is so different than the generation that was giving that advice thought it would be.

Engineering, I mean, Brad talked about this earlier, has changed dramatically.

We never had full precision and full predictability to say, your kids should do this.

In fact, many of the jobs we have today, we didn't have names for them in 1940.

So first I have a dose of humility, which is like, it was never easy to tell our kids what to do or guarantee they would listen.

For my kids, though, I reflect back on what we talked about, which is you've got to learn how to be a critical thinker and identify problems, develop a point of view to have the agency Brad's talking about.

You have to have the neuroplasticity, resilience, flexibility to be able to adapt because the world is going to change a lot.

If you think about what's happening in AI, changes to our climate, changes to geopolitics, you're going to have to adapt a lot.

And the last piece, I do think that the EQ and the financial numeracy will be really, really important as they navigate their careers.

In terms of predicting what their job title is going to be, I don't think I have any more information than my parents did, and I think they're going to be okay.

It's an interesting note that like the title may stay the same, but the work may change.

One of my favorite anecdotes was Dan Bricklin, the guy who created PhysiCalc.

He had, in the 1970s, he was a high-level programmer, extremely capable.

Then programming was changing a lot.

Then you're moving into object-oriented programming, and libraries and stuff.

And he thought that programming jobs were going to become more scarce.

And so he actually left to go get his MBA, and it was while staring at the blackboards with all the figures, he's like, "Why doesn't somebody make like an electronic spreadsheet?"

And then he invented PhysiCalc.

And it was just funny though to read how he thought that programming job was ending in the 1970s.

And I kind of think that it is changing a lot now, but you mentioned about how if you're somebody who's running an AI software tool, you're kind of managing a project.

It's project management, and having the technical skills is certainly critical.

And I've heard this a lot, like, "Why bother learning to code?"

And I'm like, "Do I want an airline pilot that doesn't know aerodynamics?"

What are other skills you think are still going to be mattering in the future?

I think the direction of travel of technology is always toward individual empowerment.

I think if you look at trend-wise, every kind of past technological revolution and every past phase change, it always drives toward the individual and what the individual is capable of.

So, you know, 1900, you had 40% of the U.S. economy working in agriculture.

Today it's 2%, right?

And we produce some multiple, more, you know, agricultural output than we did in 1900.

And you can run a large farm with a small fraction of the number of people it would have taken to run a large farm in 1900.

And so now what happens when you get that same phenomenon kind of applied widely across the economy in sectors where historically we haven't had that phenomenon, right?

And I think that there are a lot of places that would benefit from a phenomenon like that.

And that's not to say that there's an argument for job displacement, for example, but I think that the argument here is toward, you know, higher economic output kind of per unit of input.

And that fundamentally is what drives economic growth.

But people are resilient.

They find other places to go work.

And when you create the kind of local level, the micro level empowerment, you tend to create, you know, have the second and third order effects of other jobs that get created that we couldn't have foreseen, you know, in retrospect.

So, you know, it would be weird to tell someone in 1900, for example, that there are people today whose entire job is to make content for a small little device that people consume, you know, many, many hours a day, and that those people can make a perfectly kind of viable economic living.

It would seem like something that was almost kind of unimaginable that, you know, would exist, but it does.

And so there will be that kind of second set of changes and second and third order impacts.

But I think that, you know, I always kind of come back to the kind of individual empowerment point of the direction of travel being toward more people, being able to do a lot more with a lot less, and then, you know, their labor and their ideas and their creativity creating kind of the downstream opportunity for people that, you know, 20 years ago would have been doing a different job.

The example I use is in ancient Mesopotamia, 98 percent of people were in agriculture, and all of a sudden somebody invents the plow.

And if you're thinking, well, we're all farmers, we're doomed, that may have been a mindset, but the reality was that led us to inventing education and healthcare and actually governments and all these things.

And I think that that's, to your point, like that's the thing I think we sort of forget is that we've had huge, huge upheavals, like literally taking, going from 98 percent and, you know, agriculture to all of a sudden where they go.

And, you know, if we thought back in the year 1800 and said, hey, we're going to get rid of almost all farm jobs, people would be thinking, well, what are we going to do?

There's going to be massive problems.

And we realized that, like you said, we created all these new kinds of rules into higher sectors of the economy and stuff.

And it's always hard to predict, though.

It's always hard to predict where that's going to be because we just imagine the future is sort of the present, what with like shinier clothes and robots and flying cars.

And part of, I think this is part of the job of research and organizations that are close to technology, producing information to help people make the best decisions.

You know, Brad's talked to that agency.

Agency requires sort of an individual characteristic, right?

But it also will require information about what the market looks like, where technology is going.

And so I feel that as a big responsibility in what I'm doing here, you know, our mission is to benefit all humanity.

And to do that, I want to make sure people have the information they need to the best of my ability, right?

We can't predict with perfect fidelity whether it's going to be.

And I can't tell my own kids much, I'll say, and while it's those kids.

But if I can give good information based on research, that'll help people make better decisions.

And I do think ultimately find a place where they can flourish.

We should also keep in mind there's a lot of people who can't participate in the economy the way that they would like because of extenuating circumstances in their life that are, you know, born in part of things like lack of access to health care, lack of access to education.

I mean, we talked a little bit earlier about what are the impacts that we might see in parts of the developing world where access to those resources is scarce.

You know, there's the direct impact that we have of how do you make it easier for someone to scale a small business.

That's going to be a clear present and I think very positive set of things that, you know, that happen.

There's a kind of second and third order almost hidden impact that we also, I think, you know, Ronnie has the challenge of having to figure out how to measure this.

But what happens when you enable people to better manage health care, right?

Or better manage the health care of someone who is dependent on them, you know, a sibling or, you know, a parent or something like that.

What happens when you raise the education level, you know, and the educational outcome levels by 2%, right?

And what is the kind of second and third order effect of that as a downstream impact on the economy and on, you know, on people's ability to participate in the economy.

So, you know, there's kind of the direct way to look at this.

I think there's also the indirect way to look at this and that's right.

You know, I just defer to Ronnie on whatever he can measure.

I think, you know, I think it's a really good point, though, Brad, that something I've been thinking it's hard to measure, but important here is coaching, mentoring, counseling.

When he talked about people who can't fully participate in the economy, my mind immediately went to there's so many people who have so much to offer, but maybe they're neurodiverse or maybe they need a coach or someone to help them get to the next level or a level of counseling and behavioral health, which we don't have broad access to in many cases.

It's expensive.

And depending if you live in a part of the country or the world where you don't have access to, you're sort of sidelined.

And, you know, economists will use the technical term of labor force participation.

What it really means is you're sidelined.

You can't participate.

But if we can help people compared to having no help now, some help, we could help them participate in the economy more fully.

And that can unlock a lot of potential.

I do think any equation, any cost benefit analysis, any sort of reckoning about what's going to happen in the economy needs to also consider people who aren't participating the way they could now get enabled.

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I think that's a really important point.

I think that's a really important point.

I think that's a really important point.

I think that's a really important point.

I think that's a really important point.

I think that's a really important point.

I think that's a really important point.

I think that's a really important point.

I think that's a really important point.

I think that's a really important point.

I think that's a really important point.

When you make intelligence too cheap to meter and that intelligence is providing, let's say, legal advice or financial management advice or advice on real estate, all of a sudden, you get a bunch of new people accessing that.

They never could access it before.

It's opening up the market, number one.

Then once those people start making decisions, buying a property, making a transaction, engaging with legal services, they're going to have higher and higher level needs.

All of a sudden, the business that they're running is more complex or they have two properties to manage.

There's a bunch of people who are trained in those fields who never served this market before, but now they're going to come to them with more complex questions.

If that accelerates, that could create tremendous opportunities in those fields.

It will be about deciding which part of the market to focus on, what kinds of skills you want to leverage.

I think for real estate agents and insurance brokers and financial advisors, there's a potential for this to actually onboard a tremendous number of people who never would access their services to begin with.

That's the excitement of reducing the cost of intelligence dramatically, which is what's happening.

I would say an example that's, I think, very close to home here is that every time there's a new model or in some new technology from OpenAI, you'll get some pundit will go, "Well, how come they're still hiring?"

I'm a growth mentality person.

I'm like, "Well, of course they're hiring because," and I want to get a sanity check on this.

My prediction, I've told people, is more people are going to work for OpenAI after AGI than work before it.

It's not like all of a sudden, "Great.

We've got a new tool.

You don't need people doing these roles before.

The roles change, but you're going to want more people."

Do you think, would you agree about my assessment of the trajectory of OpenAI, more people here after AGI?

I think it will be more people after AGI.

I go back to what I said earlier of the demarcator of the impact of AI being about more output per person.

You get this scaled down basically in how large of a firm or company can be run by some number of people.

A large enterprise had to be run by 100,000 people before maybe that number comes down to 50,000, eventually 20,000, 5,000, 1,000, 100, and so on and so forth.

Maybe it's an even steeper falloff than that.

I suspect and hope OpenAI will be no different than that, especially given what we do.

I think going back to also the point I made around what do we see as the second and third order impacts of the deflationary aspect of intelligence.

It creates significant and disproportionate demand for the service.

What does that mean for us?

It means we need more people that can help work with more users across more use cases.

It means we need more people helping policymakers think about the problem.

It means we need a chief economist.

If you'd asked me three years ago if we would have needed a chief economist, I would have said maybe in 2030, but here we are.

I think it will be more people, but I think it is somewhat a consequence of both of the trends I just mentioned.

I am helping out a friend who's working on training models and stuff to help with cancer nutrition.

We were talking to somebody from OpenAI yesterday, and they said, "Would it be helpful to talk to the health team?"

I'm like, "You have a health team?"

I said, "Yeah, we do."

I'm like, "Well, great."

Then having a conversation with them.

That was the thing like, "Wow, what a great area of expansion."

I think that I hope that other companies are thinking about how these tools really are augment, amplify, and create opportunities for growth.

I think that if you have good talent, you want to keep that talent and find more talent, not find a way to not need talent.

I think that will put them at a disadvantage if they're not being forward thinking about that.

If you can get incredible leverage on every marginal person, 10x, 100x leverage that you could get from 10 years ago or something like that, in some sense, why wouldn't you want more people?

If Ronnie's team can now, with a team of 10 people, do economic analysis across 10 different subjects or 10 different sectors versus two, because every person now is doing three or four times more, that's an amazing thing.

It just means that we as a company are capable of doing more.

The thing that we set out, we'll handle that in 2026 or 2027.

It's like, "No, no, we can do it right now."

Do you have favorite chat CPT tips or advice to get people on using AI?

I have a few.

I think the coaching is so valuable.

You meet so many people who say, "Hi, I'm a religious chat CPT user."

You find out they're not even logged in or they don't know about deep research.

You're like, "Oh my gosh, there's so much more you can do."

For me, the coaching has been so valuable on diet and fitness.

I'm training for a big athletic adventure to play basketball at Duke at a coach cake camp.

I've requested time off.

He's got to approve it.

I got to be in good shape because otherwise, I'm going to tear my ACL the first second I get out there.

Chat CPT has helped me over the next four weeks really get in the best shape of middle age.

How is it doing that?

It's looking at the food I'm eating and giving me advice and giving me calorie breakdowns.

It's reducing the decisions I need to make by analyzing what I've had that day.

It's helping me track weight and other fitness indicators.

Doing that, I have this map out to four weeks, which would have been really, really hard with the jobs we have and the travel that we're all doing to manage.

I feel like that's a pretty simple one that you don't need super advanced tools to do, but it's really changed my outlook and made this possible.

That's my favorite one of this month.

The thing I do, especially now with O3, and I think O3 as a model broke through the barrier for me.

It crossed the chasm.

Look, all of our earlier models were great.

O3, they're something deeply great.

The thing I use it for is to actually challenge me.

A lot of my job is I'm trying to make assumptions about how things work just based on empirical observation of what companies are using us in certain ways, what users tell me they like or don't like.

In some ways, like I said early on, our job is to predict the future.

O3 has an incredible ability to actually be a question-asker.

I think people think of chat GBT as something that you can only ask questions to.

A lot of times, what I really want it to do is actually ask me questions and challenge my assumptions and make a counter argument to me of why something works or doesn't work the way I think it might work.

It's an incredibly effective thought partner in that regard.

It can be at really big things or it could be at really low-level dumb things.

I just got a puppy and I dogged my whole life.

We've had a puppy that now has been, I would say, not the easiest when it comes to getting her to calm down and go to sleep.

My wife and I could not figure out how to get her to do this.

Chat GBT being a resource for challenging our assumptions about what we thought we knew about puppy training, for example, has been an interesting experience.

O3 is something special and we talked a lot about what happens when the models can push as well as pull in, when they can get you to think about a thing.

That has been amazing.

O3 has really been a fun experience talking to.

It's the first time I felt like it's not just something that's telling me a thing that it looked up versus something I thought about.

Brad, Ronnie, thank you very much.

This has been great and I hope we can speak again in the future about this and maybe a check on the progress of all of this.

Looking forward to it.

Thanks for having us.