AI Accelerates Scientific Discovery and Economic Growth Through Augmentation

Key Quotes

"Most people will know OpenAI through the ChatGPT products but of course, you know, ChatGPT was sort of an accidental consumer product because the team at OpenAI has been working on an API for developers to build on top of AI models before that but when it came time to release this consumer product they weren't really sure how it would be received and the quick adoption almost 100 million users in just two months really was unprecedented larger than the other consumer product that I'm familiar with and really set the stage for the rapid pace of generative AI adoption we've seen over the last few years."

Ronnie Chatterji explains that ChatGPT's widespread adoption was unexpected, as OpenAI's primary focus had been on developing an API for developers. This rapid consumer uptake, reaching nearly 100 million users in two months, significantly accelerated the adoption of generative AI. Chatterji highlights this as a pivotal moment for the technology's integration into daily life and business.

"The question is what's the business model going to be and I think you're starting to see some really interesting things emerge OpenAI's products like ChatGPT have big subscription bases so some people are already paying to use ChatGPT or the API and that's obviously an important source of revenue enterprises are increasingly signed up a large percentage of enterprises around the world and this just isn't in the US this is globally are signing up for some sort of artificial intelligence product and a lot of it is ChatGPT Enterprise and so that's another model by which we'll generate revenue."

Ronnie Chatterji discusses the evolving business models for AI, noting that subscription services for products like ChatGPT and its enterprise version are becoming significant revenue streams. He points out that a substantial number of global enterprises are adopting AI products, indicating a growing market for these technologies. Chatterji suggests that these subscription models are key to generating revenue in the AI sector.

"And yeah when I think about science I think about a scientist the scientist deciding where to spend her career I'm a social scientist but some of us you know might have to make some more decisions what do you want to work on for me it was entrepreneurship and innovation but for that scientist facing that endless corridor with doors on either side she has to decide what she's going to spend her life on what lab is she going to join what skills is she going to acquire and you make those choices early in your life and a couple of postdocs later it's kind of hard to switch and I feel like what AI can do for the scientific community is try to look behind some of those doors figure out where there might be more potential help run quicker experiments more revelatory experiments and help that scientist early in her career figure out what door she wants to spend her life working behind and then unleash innovations like we've never seen that's where I think the real promise is for innovation and economic growth."

Ronnie Chatterji uses the analogy of a scientist choosing a career path to illustrate AI's potential impact on scientific innovation. He explains that AI can help researchers explore different avenues, conduct faster experiments, and identify promising areas of study earlier in their careers. Chatterji believes this capability will accelerate scientific discovery and drive significant economic growth.

"And so one how does this how do you know which pieces to put together how do you know which novel combinations are actually going to be useful and of course just combining them is not always enough you have to put them through a lot of different tests to figure out whether they're durable whether they're giving you scientific insight and so I sort of feel like AI can help us both brainstorm which combinations might be most useful and two help us run through some of those combinations and figure out which ones are most fruitful that's really the promise I think of AI in science folks working in those fields are finding really important applications in all parts of the scientific process but for me I think about it in terms of trying to brainstorm new ideas make new combinations make testing and experimentation more efficient and then when you do find things that work scaling them faster that's where I really think AI can make a big difference."

Ronnie Chatterji elaborates on AI's role in scientific research, suggesting it can assist in identifying useful combinations of ideas and testing their viability. He posits that AI can help scientists brainstorm novel concepts, make new combinations, and conduct more efficient testing and experimentation. Chatterji emphasizes that AI's ability to scale successful findings faster is a key area where it can significantly impact scientific progress and innovation.

"What's the worst use of AI? Outsourcing your critical thinking, you know, this is something I take really seriously as a professor and someone who just loves to learn. It'll be a shame if people are using this to avoid going deep or not engaging with really difficult questions and just having a chatbot spit out for you. I just, I really hope for my kids that them and their colleagues and classmates as they get older are not going to do it that way and we really have to work hard on that everybody, but that's something that's the worst possible use that I can think of."

Ronnie Chatterji identifies outsourcing critical thinking as the worst possible use of AI, expressing concern that people might use it to avoid deep engagement with complex questions. As a professor, Chatterji emphasizes the importance of learning and critical thinking, hoping that future generations will not rely on AI to bypass these essential processes. He believes this is a crucial area where society must actively work to prevent misuse.

"The best use then? So right now and I don't know if it's the best use but this is the use we did the biggest analysis so far we have 1.5 million conversations from ChatGPT and we basically find that there's really three big use cases: one is seeking information, you can think about that traditionally like a web search and that's how I found the jeans right? Then you get practical guidance, which is a really interesting one, which I think is something that economists need to think more about, but a lot of people are using AI just to inform decisions, make better decisions, right? Streamline decisions and AI as a decision assister is really I think the key place that I think it's being used really effectively now and that creates a lot of consumer surplus but doesn't necessarily show up in GDP."

Ronnie Chatterji outlines the top three use cases for ChatGPT based on a large-scale analysis of user conversations. He identifies seeking information, similar to web search, as one primary use. Chatterji also highlights practical guidance and AI's role as a "decision assister" for streamlining decision-making as highly effective applications, noting that these contribute to consumer surplus even if not directly reflected in GDP.