Reflections

The second birthday of ChatGPT was only a little over a month ago, and now we have transitioned into the next paradigm of models that can do complex reasoning. New years get people in a reflective mood, and I wanted to share some personal thoughts about how it has gone so far, and some of the things I’ve learned along the way.

As we get closer to AGI, it feels like an important time to look at the progress of our company. There is still so much to understand, still so much we don’t know, and it’s still so early. But we know a lot more than we did when we started.

We started OpenAI almost nine years ago because we believed that AGI was possible, and that it could be the most impactful technology in human history. We wanted to figure out how to build it and make it broadly beneficial; we were excited to try to make our mark on history. Our ambitions were extraordinarily high and so was our belief that the work might benefit society in an equally extraordinary way.

At the time, very few people cared, and if they did, it was mostly because they thought we had no chance of success.

In 2022, OpenAI was a quiet research lab working on something temporarily called “Chat With GPT-3.5”. (We are much better at research than we are at naming things.) We had been watching people use the playground feature of our API and knew that developers were really enjoying talking to the model. We thought building a demo around that experience would show people something important about the future and help us make our models better and safer.

We ended up mercifully calling it ChatGPT instead, and launched it on November 30th of 2022.

We always knew, abstractly, that at some point we would hit a tipping point and the AI revolution would get kicked off. But we didn’t know what the moment would be. To our surprise, it turned out to be this.

The launch of ChatGPT kicked off a growth curve like nothing we have ever seen—in our company, our industry, and the world broadly. We are finally seeing some of the massive upside we have always hoped for from AI, and we can see how much more will come soon.


It hasn’t been easy. The road hasn’t been smooth and the right choices haven’t been obvious.

In the last two years, we had to build an entire company, almost from scratch, around this new technology. There is no way to train people for this except by doing it, and when the technology category is completely new, there is no one at all who can tell you exactly how it should be done.

Building up a company at such high velocity with so little training is a messy process. It’s often two steps forward, one step back (and sometimes, one step forward and two steps back). Mistakes get corrected as you go along, but there aren’t really any handbooks or guideposts when you’re doing original work. Moving at speed in uncharted waters is an incredible experience, but it is also immensely stressful for all the players. Conflicts and misunderstanding abound.

These years have been the most rewarding, fun, best, interesting, exhausting, stressful, and—particularly for the last two—unpleasant years of my life so far. The overwhelming feeling is gratitude; I know that someday I’ll be retired at our ranch watching the plants grow, a little bored, and will think back at how cool it was that I got to do the work I dreamed of since I was a little kid. I try to remember that on any given Friday, when seven things go badly wrong by 1 pm.


A little over a year ago, on one particular Friday, the main thing that had gone wrong that day was that I got fired by surprise on a video call, and then right after we hung up the board published a blog post about it. I was in a hotel room in Las Vegas. It felt, to a degree that is almost impossible to explain, like a dream gone wrong.

Getting fired in public with no warning kicked off a really crazy few hours, and a pretty crazy few days. The “fog of war” was the strangest part. None of us were able to get satisfactory answers about what had happened, or why. 

The whole event was, in my opinion, a big failure of governance by well-meaning people, myself included. Looking back, I certainly wish I had done things differently, and I’d like to believe I’m a better, more thoughtful leader today than I was a year ago.

I also learned the importance of a board with diverse viewpoints and broad experience in managing a complex set of challenges. Good governance requires a lot of trust and credibility. I appreciate the way so many people worked together to build a stronger system of governance for OpenAI that enables us to pursue our mission of ensuring that AGI benefits all of humanity.

My biggest takeaway is how much I have to be thankful for and how many people I owe gratitude towards: to everyone who works at OpenAI and has chosen to spend their time and effort going after this dream, to friends who helped us get through the crisis moments, to our partners and customers who supported us and entrusted us to enable their success, and to the people in my life who showed me how much they cared. [1]

We all got back to the work in a more cohesive and positive way and I’m very proud of our focus since then. We have done what is easily some of our best research ever. We grew from about 100 million weekly active users to more than 300 million. Most of all, we have continued to put technology out into the world that people genuinely seem to love and that solves real problems.


Nine years ago, we really had no idea what we were eventually going to become; even now, we only sort of know. AI development has taken many twists and turns and we expect more in the future.

Some of the twists have been joyful; some have been hard. It’s been fun watching a steady stream of research miracles occur, and a lot of naysayers have become true believers. We’ve also seen some colleagues split off and become competitors. Teams tend to turn over as they scale, and OpenAI scales really fast. I think some of this is unavoidable—startups usually see a lot of turnover at each new major level of scale, and at OpenAI numbers go up by orders of magnitude every few months. The last two years have been like a decade at a normal company. When any company grows and evolves so fast, interests naturally diverge. And when any company in an important industry is in the lead, lots of people attack it for all sorts of reasons, especially when they are trying to compete with it.

Our vision won’t change; our tactics will continue to evolve. For example, when we started we had no idea we would have to build a product company; we thought we were just going to do great research. We also had no idea we would need such a crazy amount of capital. There are new things we have to go build now that we didn’t understand a few years ago, and there will be new things in the future we can barely imagine now. 

We are proud of our track-record on research and deployment so far, and are committed to continuing to advance our thinking on safety and benefits sharing. We continue to believe that the best way to make an AI system safe is by iteratively and gradually releasing it into the world, giving society time to adapt and co-evolve with the technology, learning from experience, and continuing to make the technology safer. We believe in the importance of being world leaders on safety and alignment research, and in guiding that research with feedback from real world applications.

We are now confident we know how to build AGI as we have traditionally understood it. We believe that, in 2025, we may see the first AI agents “join the workforce” and materially change the output of companies. We continue to believe that iteratively putting great tools in the hands of people leads to great, broadly-distributed outcomes.

We are beginning to turn our aim beyond that, to superintelligence in the true sense of the word. We love our current products, but we are here for the glorious future. With superintelligence, we can do anything else. Superintelligent tools could massively accelerate scientific discovery and innovation well beyond what we are capable of doing on our own, and in turn massively increase abundance and prosperity.

This sounds like science fiction right now, and somewhat crazy to even talk about it. That’s alright—we’ve been there before and we’re OK with being there again. We’re pretty confident that in the next few years, everyone will see what we see, and that the need to act with great care, while still maximizing broad benefit and empowerment, is so important. Given the possibilities of our work, OpenAI cannot be a normal company.

How lucky and humbling it is to be able to play a role in this work.

(Thanks to Josh Tyrangiel for sort of prompting this. I wish we had had a lot more time.)




[1]

There were a lot of people who did incredible and gigantic amounts of work to help OpenAI, and me personally, during those few days, but two people stood out from all others.

Ron Conway and Brian Chesky went so far above and beyond the call of duty that I’m not even sure how to describe it. I’ve of course heard stories about Ron’s ability and tenaciousness for years and I’ve spent a lot of time with Brian over the past couple of years getting a huge amount of help and advice.

But there’s nothing quite like being in the foxhole with people to see what they can really do. I am reasonably confident OpenAI would have fallen apart without their help; they worked around the clock for days until things were done.

Although they worked unbelievably hard, they stayed calm and had clear strategic thought and great advice throughout. They stopped me from making several mistakes and made none themselves. They used their vast networks for everything needed and were able to navigate many complex situations. And I’m sure they did a lot of things I don’t know about.

What I will remember most, though, is their care, compassion, and support.

I thought I knew what it looked like to support a founder and a company, and in some small sense I did. But I have never before seen, or even heard of, anything like what these guys did, and now I get more fully why they have the legendary status they do. They are different and both fully deserve their genuinely unique reputations, but they are similar in their remarkable ability to move mountains and help, and in their unwavering commitment in times of need. The tech industry is far better off for having both of them in it.

There are others like them; it is an amazingly special thing about our industry and does much more to make it all work than people realize. I look forward to paying it forward.

On a more personal note, thanks especially to Ollie for his support that weekend and always; he is incredible in every way and no one could ask for a better partner.

GPT-4o

There are two things from our announcement today I wanted to highlight.

First, a key part of our mission is to put very capable AI tools in the hands of people for free (or at a great price). I am very proud that we’ve made the best model in the world available for free in ChatGPT, without ads or anything like that. 

Our initial conception when we started OpenAI was that we’d create AI and use it to create all sorts of benefits for the world. Instead, it now looks like we’ll create AI and then other people will use it to create all sorts of amazing things that we all benefit from. 

We are a business and will find plenty of things to charge for, and that will help us provide free, outstanding AI service to (hopefully) billions of people. 

Second, the new voice (and video) mode is the best computer interface I’ve ever used. It feels like AI from the movies; and it’s still a bit surprising to me that it’s real. Getting to human-level response times and expressiveness turns out to be a big change.

The original ChatGPT showed a hint of what was possible with language interfaces; this new thing feels viscerally different. It is fast, smart, fun, natural, and helpful.

Talking to a computer has never felt really natural for me; now it does. As we add (optional) personalization, access to your information, the ability to take actions on your behalf, and more, I can really see an exciting future where we are able to use computers to do much more than ever before.

Finally, huge thanks to the team that poured so much work into making this happen!

What I Wish Someone Had Told Me

  1. Optimism, obsession, self-belief, raw horsepower and personal connections are how things get started.
  2. Cohesive teams, the right combination of calmness and urgency, and unreasonable commitment are how things get finished. Long-term orientation is in short supply; try not to worry about what people think in the short term, which will get easier over time.
  3. It is easier for a team to do a hard thing that really matters than to do an easy thing that doesn’t really matter; audacious ideas motivate people.
  4. Incentives are superpowers; set them carefully.
  5. Concentrate your resources on a small number of high-conviction bets; this is easy to say but evidently hard to do. You can delete more stuff than you think.
  6. Communicate clearly and concisely.
  7. Fight bullshit and bureaucracy every time you see it and get other people to fight it too. Do not let the org chart get in the way of people working productively together.
  8. Outcomes are what count; don’t let good process excuse bad results.
  9. Spend more time recruiting. Take risks on high-potential people with a fast rate of improvement. Look for evidence of getting stuff done in addition to intelligence.
  10. Superstars are even more valuable than they seem, but you have to evaluate people on their net impact on the performance of the organization.
  11. Fast iteration can make up for a lot; it’s usually ok to be wrong if you iterate quickly. Plans should be measured in decades, execution should be measured in weeks.
  12. Don’t fight the business equivalent of the laws of physics.
  13. Inspiration is perishable and life goes by fast. Inaction is a particularly insidious type of risk.
  14. Scale often has surprising emergent properties.
  15. Compounding exponentials are magic. In particular, you really want to build a business that gets a compounding advantage with scale.
  16. Get back up and keep going.
  17. Working with great people is one of the best parts of life.

Helion Needs You

Helion has been progressing even faster than I expected and is on pace in 2024 to 1) demonstrate Q > 1 fusion and 2) resolve all questions needed to design a mass-producible fusion generator.

The goals of the company are quite ambitious—clean, continuous energy for 1 cent per kilowatt-hour, and the ability to manufacture enough power plants to satisfy the current electrical demand of earth in a ten year period.

If both things happen, it will transform the world. Abundant, clean, and radically inexpensive energy will elevate the quality of life for all of us—think about how much the cost of energy factors into what we do and use. Also, electricity at this price will allow us to do things like efficiently capture carbon (so although we’ll still rely on gasoline for awhile, it’ll be ok).

Although Helion’s scientific progress of the past 8 years is phenomenal and necessary, it is not sufficient to rapidly get to this new energy economy. Helion now needs to figure out how to engineer machines that don’t break, how to build a factory and supply chain capable of manufacturing a machine every day, how to work with power grids and governments around the world, and more.

The biggest input to the degree and speed of success at the company is now the talent of the people who join the team. Here are a few of the most critical jobs, but please don’t let the lack of a perfect fit deter you from applying.

Electrical Engineer, Low Voltage: https://boards.greenhouse.io/helionenergy/jobs/4044506005
Electrical Engineer, Pulsed Power: https://boards.greenhouse.io/helionenergy/jobs/4044510005
Mechanical Engineer, Generator Systems: https://boards.greenhouse.io/helionenergy/jobs/4044522005
Manager of Mechanical Engineering: https://boards.greenhouse.io/helionenergy/jobs/4044521005

DALL•E 2

Today we did a research launch of DALL•E 2, a new AI tool that can create and edit images from natural language instructions. 

Most importantly, we hope people love the tool and find it useful. For me, it’s the most delightful thing to play with we’ve created so far. I find it to be creativity-enhancing, helpful for many different situations, and fun in a way I haven’t felt from technology in a while.

But I also think it’s noteworthy for a few reasons:

1) This is another example of what I think is going to be a new computer interface trend: you say what you want in natural language or with contextual clues, and the computer does it. We offer this for code and now image generation; both of these will get a lot better. But the same trend will happen in new ways until eventually it works for complex tasks—we can imagine an “AI office worker” that takes requests in natural language like a human does.

2) It sure does seem to “understand” concepts at many levels and how they relate to each other in sophisticated ways.

3) Copilot is a tool that helps coders be more productive, but still is very far from being able to create a full program. DALL•E 2 is a tool that will help artists and illustrators be more creative, but it can also create a “complete work”. This may be an early example of the impact AI on labor markets. Although I firmly believe AI will create lots of new jobs, and make many existing jobs much better by doing the boring bits well, I think it’s important to be honest that it’s increasingly going to make some jobs not very relevant (like technology frequently does).

4) It’s a reminder that predictions about AI are very difficult to make. A decade ago, the conventional wisdom was that AI would first impact physical labor, and then cognitive labor, and then maybe someday it could do creative work. It now looks like it’s going to go in the opposite order.

5) It’s an example of a world in which good ideas are the limit for what we can do, not specific skills.

6) Although the upsides are great, the model is powerful enough that it's easy to imagine the downsides.

Hopefully this summer, we’ll do a product launch and people will be able to use it for all sorts of things. We wanted to start with a research launch to figure out how to minimize the downsides in collaboration with a larger group of researchers and artists, and to give people some time to adapt to the change—in general, we are believers in incremental deployment strategies. (Obviously the world already has Photoshop and we already know that images can be manipulated, for good and bad.)

 (A robot hand drawing, by DALL•E)


Helion

I’m delighted to be investing more in Helion. Helion is by far the most promising approach to fusion I’ve seen.

David and Chris are two of the most impressive founders and builders (in the sense of building fusion machines, in addition to building companies!) I have ever met, and they have done something remarkable. When I first invested in them back in 2014, I was struck by the thoughtfulness of their plans about the scientific approach, the system design, cost optimizations, and the fuel cycle.

And now, with a tiny fraction of the money spent on other fusion efforts but the culture of a startup, they and their team have built a generator that produces electricity. Helion has a clear path to net electricity by 2024, and has a long-term goal of delivering electricity for 1 cent per kilowatt-hour. (!)

If this all works as we hope, we may have a path out of the climate crisis. Even though there are a lot of emissions that don’t come from electrical generation, we’d be able to use abundant energy to capture carbon and other greenhouses gases.

And if we have much cheaper energy than ever before, we can do things that are difficult to imagine today. The cost of energy is one of the fundamental inputs in the costs of so much else; dramatically cheaper energy will lead to dramatically better quality of life for many people.

The Strength of Being Misunderstood

A founder recently asked me how to stop caring what other people think. I didn’t have an answer, and after reflecting on it more, I think it's the wrong question.

Almost everyone cares what someone thinks (though caring what everyone thinks is definitely a mistake), and it's probably important. Caring too much makes you a sheep. But you need to be at least a little in tune with others to do something useful for them.

It seems like there are two degrees of freedom: you can choose the people whose opinions you care about (and on what subjects), and you can choose the timescale you care about them on. Most people figure out the former [1] but the latter doesn’t seem to get much attention.

The most impressive people I know care a lot about what people think, even people whose opinions they really shouldn’t value (a surprising numbers of them do something like keeping a folder of screenshots of tweets from haters). But what makes them unusual is that they generally care about other people’s opinions on a very long time horizon—as long as the history books get it right, they take some pride in letting the newspapers get it wrong. 

You should trade being short-term low-status for being long-term high-status, which most people seem unwilling to do. A common way this happens is by eventually being right about an important but deeply non-consensus bet. But there are lots of other ways–the key observation is that as long as you are right, being misunderstood by most people is a strength not a weakness. You and a small group of rebels get the space to solve an important problem that might otherwise not get solved.


 

[1] In the memorable words of Coco Chanel, “I don’t care what you think about me. I don’t think about you at all.”

PG and Jessica

A lot of people want to replicate YC in some other industry or some other place or with some other strategy. In general, people seem to assume that: 1) although there was some degree of mystery or luck about how YC got going, it can’t be that hard, and 2) if you can get it off the ground, the network effects are self-sustaining.

More YC-like things are good for the world; I generally try to be helpful. But almost none of them work. People are right about the self-sustaining part, but they can’t figure out how to get something going.

The entire secret to YC getting going was PG and Jessica—there was no other magic trick. A few times a year, I end up in a conversation at a party where someone tells a story about how much PG changed their life—people speak with more gratitude than they do towards pretty much anyone else. Then everyone else agrees, YC founders and otherwise (non-YC founders might talk about an impactful essay or getting hired at a YC company). Jessica still sadly doesn’t get nearly the same degree of public credit, but the people who were around the early days of YC know the real story.

What did they do? They took bets on unknown people and believed in them more than anyone had before. They set strong norms and fought back hard against bad behavior towards YC founders. They trusted their own convictions, were willing to do things their way, and were willing to be disliked by the existing power structures. They focused on the most important things, they worked hard, and they spent a huge amount of time 1:1 with people. They understood the value of community and long-term orientation. When YC was very small, it felt like a family.

Perhaps most importantly, they built an ecosystem (thanks to Joe Gebbia for pointing this out). This is easy to talk about but hard to do, because it requires not being greedy. YC has left a lot of money on the table; other people have made more money from the ecosystem than YC has itself. This has cemented YC’s place—the benefits to the partners, alumni, current batch founders, Hacker News readers, Demo Day investors, and everyone else around YC is a huge part of what makes it work.

I am not sure if any of this is particularly useful advice—none of it sounds that hard, and yet in the 15 years since, it hasn’t been close to replicated.

But it seems worth trying. I am pretty sure no one has had a bigger total impact on the careers of people in the startup industry over that time period than the two of them.

Researchers and Founders

I spent many years working with founders and now I work with researchers.

Although there are always individual exceptions, on average it’s surprising to me how different the best people in these groups are (including in some qualities that I had assumed were present in great people everywhere, like very high levels of self-belief).

So I’ve been thinking about the ways they’re the same, because maybe there is something to learn about qualities of really effective people in general.

The best people in both groups spend a lot of time reflecting on some version of the Hamming question—"what are the most important problems in your field, and why aren’t you working on them?” In general, no one reflects on this question enough, but the best people do it the most, and have the best ‘problem taste’, which is some combination of learning to think independently, reason about the future, and identify attack vectors. (This from John Schulman is worth reading: http://joschu.net/blog/opinionated-guide-ml-research.html).

They have a laser focus on the next step in front of them combined with long-term vision. Most people only have one or the other.

They are extremely persistent and willing to work hard. As far as I can tell, there is no high-probability way to be very successful without this, and you should be suspicious of people who tell you otherwise unless you’d be happy having their career (and be especially suspicious if they worked hard themselves).

They have a bias towards action and trying things, and they’re clear-eyed and honest about what is working and what isn’t (importantly, this goes both ways—I’m amazed by how many people will see something working and then not pursue it). 

They are creative idea-generators—a lot of the ideas may be terrible, but there is never a shortage.

They really value autonomy and have a hard time with rules that they don’t think make sense. They are definitely not lemmings.

Their motivations are often more complex than they seem—specifically, they are frequently very driven by genuine curiosity.

Project Covalence

Almost every company and non-profit working on COVID-19 that I offered to help asked for support with clinical trials—for companies focusing on developing novel drugs, vaccines, and diagnostics, rapidly spinning up trials is one of their biggest bottlenecks. 

Science remains the only way out of the COVID-19 crisis. Dramatically improving clinical trials, which are usually time-consuming and cost tens to hundreds of millions of dollars, is one of the highest-leverage ways to get out of it faster.  

The goal of this project, in collaboration with TrialSpark and Dr. Mark Fishman, is to offer much better clinical trial support to COVID-19 projects than anything that currently exists.

Project Covalence’s platform, powered by TrialSpark, is uniquely optimized to support COVID-19 trials, which are ideally run in community settings or at the patient’s home to reduce the burden placed on hospitals and health systems. Project Covalence is well-positioned to tackle the operational and logistical challenges involved in launching such trials, and supports trial execution, 21 CFR Part 11 compliant remote data collection, telemedicine, biostatistics, sample kits for at-home specimen collection, and protocol writing. 

Researchers across academia and industry can leverage this shared infrastructure to rapidly launch their clinical trials. To facilitate coordination between studies, we will also be creating master protocols for platform studies to enable shared control arms and adaptive trial designs.

If you’re interested in getting involved or have a trial that needs support, please get in touch at ProjectCovalence@trialspark.com or visit www.projectcovalence.com.