Ought Raises $3.8 Million

We’re excited to announce new donations totaling $3.8MM from the following donors:

We are grateful to be working with these philanthropic partners. While each of our donors has their own values and beliefs, we find that as a group they take a long-term and “hits-based” approach to giving. They consider where society will be decades from now, the opportunities and risks we will face along the way, and what they can do now to make the long-term future better. This in turn allows us to focus on what we think is right from a long-term perspective. Our donors recognize that Ought is an unusual research-product hybrid organization. They’re not afraid to engage with the knotty details of our research to collaborate on ways to maximize impact in the long term while measuring signs of progress in the short term.

Our partners’ support allows us to build towards a future world where individuals and organizations arrive at better answers to life's messy but important questions. There are many of these, like

  • How do I decide whether to buy a house, save for retirement, or pay off my student loans?
  • Should I pick a job that allows me to directly work on the problems I care about now, or one that will teach me skills to be more effective at solving that problem later?
  • How should we set our hiring plan for the next 3-5 years given sales forecasts, competition, and other macroeconomic trends?
  • How should we prioritize among this set of products or features we want to launch?
  • I really like bread. Is it really that bad to be eating so much bread?

At first glance, each of these questions seems unique. But zooming out, answering them depends on a common core of reasoning. When people answer these questions today, they often

  • Think about their values and preferences
  • Gather evidence
  • Compare and weigh the evidence
  • Think through plans by comparing different paths or outcomes, making forecasts, or considering the likelihood of different scenarios

As AI and machine learning advance, we want to delegate parts of these processes to machines, especially the parts that machines do better, such as searching over a hundred thousand permutations of paths, gathering evidence from every single page on the Internet, and making forecasts based on gigabytes of data.

To successfully delegate some of this thinking to machines, we need to design systems that help us evaluate work that is too complex for us to evaluate directly. These systems also need to scale. They need to flexibly absorb diverse inputs and productively convert computation into better thinking and decision-making. AI has the potential to make answering these questions 100x or 1000x better.

But there's no guarantee that we’re headed towards this world. There are many pressures to build AI systems that optimize for quick, plentiful reward signals like retweets or video views - signals that look appealing instead of actually being good. It’s more challenging to think carefully about good reasoning and how it can help people figure out the answers they'd give if they had more time to think.

So we and our donors are taking the long-term view and building towards this world. Today, we're working on this problem in small-scale settings, mostly with human experts, but we're developing mechanisms designed to hold up even for very large-scale machine reasoning. If this is the kind of world you want to build too, get in touch.

This post was published on February 14, 2020 by Jungwon Byun.