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.)

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.

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.”

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.

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.

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.