14 Mar 2023 · 25m · Curt Nickisch, Ethan Mollick · 19 quoted facts
Ethan Mollick argues that ChatGPT and similar tools are a real productivity breakthrough, and that companies should start experimenting with them now. He says early studies show 30 to 50 percent gains, individuals can use the tools with no corporate rollout, and waiting for a finished strategy or clear legal rules means falling behind.
The tension is between speed and certainty. Mollick says no one, including R&D teams and consultants, knows how best to use the tools, so firms must learn by trying them in daily tasks. Yet the tools invent facts, raise ethical and legal questions, and may reshape hiring and jobs in ways nobody can yet predict.
Mollick says GPT-3 had existed for over a year and produced mediocre work. Larger models with extra tuning plus a chat format pushed performance from a 'D minus' student to 'B minus', and 'A minus' for Bing.
Two controlled experiments, one on business writing and one on coding, found these gains even for untrained users who simply pasted text into the tools. Mollick compares that with about 25% from steam power in 1800s plants, though the studies are preliminary.
Because individuals can use ChatGPT with no corporate setup, Mollick says every company has hidden users who have cut their work by two thirds. Bans like J.P. Morgan's are easy to work around with a phone.
Mollick suggests tasks like a company-wide hackathon with cash prizes, and having teams document use cases and test how much human oversight is needed. Policy then comes from what is found, not from waiting for an outside expert.
Mollick explains that the model tries to please the user, so when it lacks information it makes up plausible but wrong answers. Users should begin in areas they know well, and avoid high-stakes uses like doctors writing patient notes.
Ethan Mollick says the underlying technology is not new, but the models grew large enough to cross a threshold of ability. Adding tuning and a chat interface made them far more useful.
Mollick cites two early controlled experiments, one on business writing and one on coding, that found 30 to 50 percent productivity gains. He notes these studies are preliminary and not yet peer-reviewed.
Mollick says to experiment now rather than wait for a strategy. Ask teams to try it on daily tasks, record what works, and build policy from those cases, perhaps through a hackathon with prizes.
A hallucination is when the AI confidently makes up facts. Mollick says it happens because the system tries to give an answer that makes you happy, so you must check its work.
Mollick says nobody knows yet. He lays out four scenarios: it lifts everyone, it lifts top performers most, it levels out weak and strong workers, or it favors people skilled at prompting.
Stated by the speakers. Each quote matches the transcript, which shows what was said, not that it is true.
Ethan Mollick says GPT-3, the base generation of the technology behind ChatGPT, had existed for over a year before ChatGPT and produced only okay work.
“GPT three, which is, uh, the sort of base generation of this technology has been around for over a year, and it was okay, right?”
Ethan Mollick says the models went from D-minus to B-minus performance with ChatGPT, and an A-minus level with an internet-connected model like Bing.
“Suddenly, they became B minus students in classes, and now, if you use a even more advanced model like Bing that's connected to the internet, now they're A minus students.”
The first productivity results on these tools came from two controlled experiments, one on business writing and one on coding.
“one is a controlled experiment that was done with people writing business writing, the other was a controlled experiment with coding”
Ethan Mollick says an American plant adding steam power in the 1800s raised productivity by about 25%, which he uses as a comparison for the 30 to 50% gains seen with ChatGPT.
“the, uh, change that happened in productivity when a American plant added steam power in the eighteen hundreds was about 25% in productivity”
Host Kurt Nikish notes the early ChatGPT productivity studies are preliminary and not yet peer-reviewed.
“These are preliminary studies, they are not peer-reviewed yet, but what kind of applications are being opened up specifically in this moment”
Ethan Mollick says ChatGPT reached 100 million users faster than any product known in history.
“it was the fastest product to chat GPT to 100 million users of any product we know of in history”
Ethan Mollick says programmers have shifted from Stack Exchange to ChatGPT as their way of deciding what to code next.
“everyone in programming is using this to help with coding, right? So, that's sort of moved from Stack Exchange to ChatGPT, uh, as their method of figuring out what to code next.”
At the time of recording, J.P. Morgan had banned the use of ChatGPT in the office.
“there are a number of organizations, I know, like J. P. Morgan, at at the time we're talking here, banned the use of, uh, ChatGPT in the office”
Ethan Mollick gives the example of using Bing AI to learn Porter's five-forces framework and research the precision agriculture industry, saving about twelve hours of work.
“you could tell it to learn how to do a Porter's five-forces analysis and then look up everything about the precision agriculture industry”
Ethan Mollick says a controlled study found Google's medical AI model gave advice at least equal to doctors' advice for common illnesses.
“some of the fine-tuned models, like Google's medical model, is giving in, uh, you know, another controlled study is giving advice that's at least equal to that of doctors to patients with common illnesses”
Ethan Mollick says ChatGPT achieves top scores on every creativity test available, though he says it is not sentient.
“It maxes out every creativity test we have, even though it's not really sentient, right?”
Ethan Mollick raises as one hiring scenario the idea of a prompt engineering role, where people skilled at getting AI to work are far more productive.
“this idea that there's some prompt engineering role in the future, that some people are just good AI whisperers”
Ethan Mollick explains these AI systems predict the next word in response to a prompt while trying to please the user, which is why they sometimes make things up.
“they're predicting the next word that to to give you, the next token to give you in response to your prompt, and they're doing that to try and maximize your happiness”
Ethan Mollick says he used AI to create a credible deepfake video of himself giving a speech he never gave, in about four minutes.
“I was able to, using AI, create a comp- a a very credible, uh, deep fake video of me giving a speech I never gave in my voice, and it took about four minutes to do, right?”
Ethan Mollick expects OpenAI to release GPT-4, a model much larger than the GPT-3.5 models behind ChatGPT.
“there is going to be elements of what's called GPT four release, which is a model that's much larger than the ChatGPT GPT 3.5 models we've been playing with”
Ethan Mollick says Google has by far the largest AI research team and has been cautiously rolling out products.
“Google has by far the largest research team on AI, and they've been cautiously rolling out products and have specialized products in different industries that might happen soon.”
Ethan Mollick names Anthropic's Claude as another large language model available alongside ChatGPT.
“there's other companies, like Anthropic has Claude, which you can get access to.”
Ethan Mollick says diffusion-based models, used for example in DALL-E, create images from text and may soon do the same for video.
“there are these what are called diffusion-based models, which are often used for creating images.”
Host Kurt Nikish says businesses and individuals began widely discussing ChatGPT's potential to change work in late 2022.
“Late in 2022 is when businesses of all sides and individuals started talking about how this easily accessible type of AI really was poised to change work as we knew it, really this time.”
Mollick explains that bigger models, not new technology, pushed AI past a usefulness threshold.
“somehow, we crossed a threshold of ability with the size of these models”
Two controlled experiments suggest gains far larger than past technologies, which is why leaders are paying attention.
“they have both found improvements in thirty to 50% improvements in productivity”
Because individuals can adopt the tool alone, companies should assume staff already use it and respond urgently.
“there are secret users of ChatGPT in every company, I guarantee you”
Mollick urges firms to test uses and consider how rivals might attack their business model.
“I think that this is a war room situation because the situation's evolving so quickly”
Humans and AI working together shift the worst tasks to the machine, and hiring depends on which of four scenarios holds.
“people who use ChatGPT in these preliminary studies are happier because they offload the worst parts of their job”
Concrete uses such as memos, agendas and meeting summaries show how quickly the tool can be tried.
“You have to try this. I mean, it's the only way to work.”
The tool invents facts, raises plagiarism and misinformation concerns, and prompts questions about who gains from automation.
“you have to be very careful to use this in areas you know well to start off with”
GPT-4, Google, Anthropic and image and video models suggest the pace will keep rising.
“Google has by far the largest research team on AI”
Compliance questions will stay unsettled, but Mollick says to experiment ethically and legally anyway.
“to not do experimentation because you're waiting for clarity seems like a bad idea”
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Welcome to the HBR IdeaCast from Harvard Business Review. I'm Kurt <sty:stt:vocal_texture_delivery> Nikish. <sty:end:vocal_texture_delivery> You don't have to be at the cutting edge of your industry or profession to be working on artificial intelligence. You've probably already heard the term ChatGPT, that is, if you're not using it already yourself. Late in 2022 is when businesses of all sides and individuals started talking about how this easily accessible type of AI really was poised to change work as we knew it, really this time.
ChatGPT has burst into public consciousness by making it much easier for anyone to use it and see its potential, and use cases have been making headlines for reasons good and bad. Even with this rapid distribution, companies are still very far behind in developing strategies, policies, and best practices, and that needs to change quickly, according to today's guest. He says the use of ChatGPT AI could have something like 50% productivity increase for some workers. Companies need to know how to harness that power, and they need to develop a strategy fast.
Hello, nice to be here.
So, from your perspective, what exactly is different about this technology or this ecosystem this time around?
So, actually, the fact there's no ecosystem is part of what's interesting. But the technology's not new, right? GPT three, which is, uh, the sort of base generation of this technology has been around for over a year, and it was okay, right? You'd look at it, and it was a distinctly sort of D minus student, uh, it produced okay work. And then when the chatbot came out and the associated 3.5 model, there were just larger models with some additional tweaking, but not radical changes in technology.
But somehow, we crossed a threshold of ability with the size of these models that have made them incredibly more useful. Suddenly, they became B minus students in classes, and now, if you use a even more advanced model like Bing that's connected to the internet, now they're A minus students. So, there's something about the size of the model interacting with the kind of information that's in there and the chat, uh, method that has made them much more useful, uh, suddenly.
It's a radical change in a very short period of time. We have the first early results on productivity from these tools, and one is a controlled experiment that was done with people writing business writing, the other was a controlled experiment with coding, and they have both found improvements in thirty to 50% improvements in productivity, and that's for people not trained on the system, not using optimized systems, just pasting things into ChatGPT and its related tools like Copilot and getting results out of it.
So, something clearly is big here. We don't see productivity improvements like that, right? To to give you context, the, uh, change that happened in productivity when a American plant added steam power in the eighteen hundreds was about 25% in productivity. So, these are very big numbers in an initial pass. It's not just sort of anecdotal now, we have data that suggests that something big is happening.
Got it. These are preliminary studies, they are not peer-reviewed yet, but what kind of applications are being opened up specifically in this moment for for companies?
There is a lot, right? What's interesting is is that there is no rulebook or manual to work from. You have to explore opportunities and options yourself. So, obviously, writing computer code and writing documents, it's terrific at, right? You want to write a letter of recommendation? You want to write a performance review? It'll do an amazing job at all those things. Uh, when you start to incorporate the internet with being AI and some of the new AIs that are coming our way, it will do things like you could tell it to learn how to do a Porter's five-forces analysis and then look up everything about the precision agriculture industry and give me a table with the growth rates of every company,
their strengths and weaknesses from a five-forces perspective, and save you twelve hours of work to build a document that you can build on because you do have to check the work of AI. Um, it will generate marketing ideas for you, it's very, very good at idea generation, uh, just a a very wide range of tasks.
A lot of those sound like applications for individuals, I guess maybe that's where it starts, right? A lot of those sound like they are applications for individuals within a company setting, rather than, like, a, you know, a departmental application or or effort.
Well, that's part of what makes this so interesting, though, right, is that we are in a moment where the technology is released to individuals, and so, it was the fastest product to chat GPT to 100 million users of any product we know of in history, and the only people who can use are individuals. There's no sort of, you know, benefit to large-scale, you know, corporate use at this point. I mean, at the individual level, there is, but corporations don't have a huge advantage.
You can't call up a consulting company and have them tell you how to implement this. It doesn't require all of those additional technologies that would if you were implementing Salesforce or some other, you know, system application. So, uh, there will be more and more corporate organizational use, but when you talk about a performance difference of, you know, 30% to 80%, uh, on your time, and an individual can get that today in their work, uh, there are secret users of ChatGPT in every company, I guarantee you, who have cut their work by two thirds and don't have anyone tell about it or don't want to tell anyone about it.
I am hearing examples all the time about this. So, lots of people, when you spend about an, you have to spend about an hour to working with these systems to kind of know what they work, how they work and what they're good at and what they're bad at. But, um, I've talked to tons of people who have, you know, first of all, everyone in programming is using this to help with coding, right? So, that's sort of moved from Stack Exchange to ChatGPT, uh, as their method of figuring out what to code next.
But, as I increasingly talk to managers who have actually just automated things like, you know, like, the review process is real, like grant applications or regulatory compliance, I've seen people do, um, a whole range of different applications out there. It you'd be surprised. It's a general-purpose technology. You know, I I didn't have it right when I'm telling you now, but I I could have done that.
You just mentioned that there are people doing this in every company and companies don't even realize it. What do they need to know, and what do they need to start developing policies and strategies for?
It's a really interesting question, right? So, there are a number of organizations, I know, like J. P. Morgan, at at the time we're talking here, banned the use of, uh, ChatGPT in the office, but I'm willing to bet there's tons of people who are just doing chat on their phone and emailing it to themselves and making changes. Like, when you have a technology that cuts your time by a considerable margin, and the really interesting thing is, it cuts the most boring parts of your work, you're gonna figure out a way to use it.
So, I think companies need to realize that there isn't somebody who knows anything, but at the same time, this technology is happening so quickly, and the performance improvements are so large that, uh, you need to figure something out, and you can't wait by just writing a strategy document and seeing what happens in the industry. I would think about, what is our crash research pro program and how this works for us? Who in our organization is experimenting with this?
Should we, you know, offer some sort of insanely large prize and have everyone spend four hours next Monday hacking away at how they can automate their jobs with ChatGPT and giving large cash prizes to people who come up with the best ideas? There is no outside organization to turn to here. This is a company thing. So, I think people really need to assess their exposure to this and understand where it could benefit them.
The way you described that, it sounds like companies that have a pretty strong innovation framework and, you know, ways to bring it out within their organization are gonna be better set up to get rolling with this.
I don't mean to keep saying the same answer, but we actually don't know, right? The issue is that because, as we talked about earlier on, this is really, you know, it's an individual interaction. You basically gained a teammate, everybody did, an infinitely, uh, sort of, uh, patient, uh, intern that sometimes lies to you. Like, what do you do with that? Is is a question that it's not clear a traditional innovation framework is is helpful because your R and D team, wherever that is, doesn't have an advantage over a line manager who's using this or a customer service rep who's using this.
You used the word crash before. Should some companies even be in in triage mode? Like, this is, uh, you know, bring back the war room from the pandemic.
I I think this is as big a deal, um, in the medium term. I absolutely think you should be, right? So, you know, pick a topic, idea generation, writing, you know, what's happening there, right? And then what's the sort of red team approach? What? How could other people use this to attack your business model? And so I think that this is a war room situation because the situation's evolving so quickly, um, that I think it's very possible for, you know, kind of wait and use your usual approach to new technology and be cynical about this, and I totally get why people would be cynical about the latest new tech, uh, and miss the opportunity in front of us.
How do you, as an organization, start using this technology fairly? Should workers have to use it if they haven't already started?
So, I don't know about, you know, forcing people to use it, especially if there's no corporate use case, right? So, the policy will come from the use case, and in the cases where there's high levels of accuracy required, then the current versions of chat are probably not gonna be what you need. But, already, for example, some of the fine-tuned models, like Google's medical model, is giving in, uh, you know, another controlled study is giving advice that's at least equal to that of doctors to patients with common illnesses, and other doctors are getting the answers from the medical AI as being less dangerous than the answers from other doctors.
So, the you know, there's going to be specialized disruption coming next, which is part of why I think the general-purpose technology needs to be understood so quickly.
That process makes sense to me, you know, on an abstract level. Can you give an example of a specific company, some use case, and maybe how that could play out, just as an example?
Sure. Let's say you are a company that does, um, that's a marketing consulting company. One set of stuff you do is market research. Another set you do is perhaps writing articles on behalf of your clients to help them increase their search order, SEO optimization. You might also, you know, provide, um, you know, creative services. So, what I would be doing is asking my people in those fields to start using chat and documenting use cases, right? So it, and you'll start to build policy out of that.
So, for example, I would start any creative endeavor that I had with, uh, using ChatGPT, having a seat at the table. And I do work on creativity and innovation, uh, it adds a huge amount to any innovative process, cause it can generate tons of ideas, it can help you play off ideas, it can find novel combinations. Effectively, it's creative, by the way. It maxes out every creativity test we have, even though it's not really sentient, right?
So, try that. What is that? How does that work? Now, let's talk about another piece that you might do, which is that SEO article writing. Try using this to actually write the SEO articles. How much human o oversight is required? You know, how much do you want to let chat do the writing, and how much should it be a combination of chat and humans, which usually works better? And then let's talk about that last piece of doing marketing consulting.
Let's use Bing AI for research. How often is it right and how often is it wrong? How much time is it saving us as a research partner? So, I think you have to turn everybody into your organization into scientists in some way. Let's experiment. Let's see what works and what isn't working, but let's take our daily tasks and see how AI helps us do those things.
Yeah. It really sounds like a tremendous opportunity for hybrid work, right? You're talking about another teammate at the table or an intern who, um, you know, kind of in the same way, you go through your old emails to to write kind of the same thing again. This is basically going out and and and doing that kind of stuff for you. But you also have to exercise human judgment, you have to make decisions about what to use and what to leave out.
It sounds like the the possibilities really are for not automation, but hybrid work where humans are interacting with this technology to be more productive and and, you know, make better work.
And and also offload the worst parts of their work. So people who use ChatGPT in these preliminary studies are happier because they offload the worst parts of their job and get to do the interesting creative stuff. The more creative and interesting tasks stay in human hands, the less creative and interesting tasks hand out to these chatbots and other AIs. And the one caveat, of course, is that's today's technology. So this is improving rapidly enough that it's not clear where humans are in the loop in the future, and that's both, you know, exciting and super worrying.
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What kind of implications does this have for hiring?
So, I think part of the question with hiring is, what does ChatGPT do to skills? And I think it's gonna depend on the organization and the setting, but you can think about four different scenarios. One scenario is that it makes everybody more productive. We have some early evidence that this seems to be the case, and if that's true, then that's great, but it might affect how, you know, how many you're try to hire very high talent people who can kind of run these chatbots because they can do more of their own work.
The second option is it makes your best workers more productive. The people who are already good at work are best at using chat in this kind of hybrid mode or whatever other AIs are out there, in which case you want to make sure you're kind of hiring more stars and thinking about other lo- how many lower-level hires are are required. There's an option that it could end up sort of leveling work. So, the worst performers do just as well as good performers now because chat is helping them solve problems, and that has implications for who you're hiring.
And the final option is that this idea that there's some prompt engineering role in the future, that some people are just good AI whisperers, and if you could find and hire those people, they'll have many times more productivity, even if they're not the best traditional worker because they're the best at getting AI to do work for them. And I think you're gonna have to f- figure out which of these scenarios works for each kind of job you have in your company.
If I'm a manager leading a team, not necessarily somebody at the organizational decision level, what should I be doing right now?
Uh, I think you should be in the middle of this podcast, opening a chat window, trying to do as much of your work through chat for the day as possible. And, you know, experimenting with your job and seeing what it automates for you and what you feel comfortable with, right? So, the most tedious parts of your task, right? Writing memos, uh, it's incredible at writing memos and other information. It's great at helping you run meetings. You could say, I want to have a meeting with these four people about this topic.
Um, do the research on the science of meetings and give me a, uh, the steps I should follow, and it will give you the steps. And you could say, Okay, great. Uh, the first thing you said is create an agenda. Create an agenda for me, and it will create an agenda. And you could say, great, create the emails I need to send to each of the people on the team for the agenda. It will do that.
Then, during the meeting, you could take the transcript and just text-to-speech, paste it in afterwards and say, Summarize the meeting notes for me, and give me action points, and write an email for each of the people with an action point, and we'll do a pretty good job of that. You have to try this. I mean, it's the only way to work. And what I find is that when you have that aha moment, and people tend to have it at different points, suddenly you're like, Wow, this changes my job.
What do you say to anybody, like, who's who's intimidated by by trying this out, or just worried about the implications it has for their own work?
I think anyone who's afraid of this cause it's technology or AI shouldn't be, right? There is no advantage really to being a coder here, not even really a huge advantage to understanding how large language models work when learning to use this for your own system. These are tools that you program in English language prose. You literally give it instructions, Hey, write a four-page market research report for me using the following major bullet points, write it in an academic but authoritative style, use clear metaphors, and it will do that, right?
So, there's no reason not to dive in or to be intimidated in that way. In terms of long-term career impacts, I don't think we know the answers to that. I mean, in general, new technologies tend to, you know, first disrupt jobs and then add jobs. I'm hopeful that that's the case here, but we haven't really seen a leap in technology like this. So, one of the things that I advise workers to do is to figure out how much of their own job they can automate, that both makes their life easier, but also gives them a sense of their exposure and risk to these technologies.
I think everybody should be trying to use it for work. The problem is when people first use ChatGPT, they often bounce off it or stop using it very quickly because the first things they always do give the worst possible answers, right? So, the first thing you tend to do with this is you tend to use it like a Google search, and it doesn't know anything, it's not connected to the internet, so you get kind of frustrated.
Then maybe you try and treat it like, you know, Alexa or Siri, and you ask it kind of, you know, fun questions, and it doesn't have much of a fun personality, so you bounce off of it for that reason. Then maybe you ask it a factual question about something you know, like, what's my biography? And it makes stuff up, it hallucinates, and you're like, This is terrible. Why is anyone using this? I the the issue is that that's not how you use this tool, right?
What do you use this tool for is as you start to explore more with it, you realize it can automate huge amounts of the writing and other tasks. You can paste in, you know, a resume, and, you know, say, write a letter of recommendation that makes these three points, and it will do that for you. You could, you know, say, go out for being, you know, go out and summarize the research on this topic and give me answers to it, and it will do that for you.
You could say, come up with a hundred ideas for my marketing slogan for my company, then here's the company description, and it will do that for you. Like, these sorts of use cases start to be transformative, but they're not what we do instinctively.
Are there any dangers you would warn people of? Any disadvantages?
I mean, there's tons of risks, right? Um, so, the one thing we already talked about that's the sort of most obvious f- in your face risk is hallucination. So, you can think about these AIs as trying to do a bunch of different things. They're trying to give you an answer, they're predicting the next word that to to give you, the next token to give you in response to your prompt, and they're doing that to try and maximize your happiness, to give you something that makes you happy, give you something that's accurate, and give you something that doesn't break the moral guidelines that, uh, OpenAI and Microsoft and other companies have placed on these systems.
And so, in its attempt to do number one, to make you happy, it will often make things up. So, if you ask it for information it doesn't have access to, it will make up a completely plausible, completely convincing argument that's wrong. So, the first thing is you have to be accountable for content. As you start to use these more, you understand what the limits are, but you will find it just makes up facts left and right.
So, you have to be very careful to use this in areas you know well to start off with, right? The second, you know, issues are really start to be issues of, in general, kind of morality and ethics, right? These systems are trained using the entire internet. Does that mean that they're plagiarizing work? Not directly. You're not using someone else's words, but, you know, what does that mean? Um, how do we keep these systems ethical? Uh, I was able to, using AI, create a comp- a a very credible, uh, deep fake video of me giving a speech I never gave in my voice, and it took about four minutes to do, right?
So, misinformation, chatbots are very convincing, you could think they're human. How do we deal with that? And then there's the larger larger scale work implications. So, what does it mean if you automate people's tasks? You know, are are who gets the benefit from that? Is that the workers or the companies? And then there's the even larger implications of what do we do in a world where there are, you know, machines that could do some of these, you know, without being sentient, some of these intelligent tasks, and how do we deal with their growth and ability over time?
So, I think there's a lot of concerns, um, all across the board that we need to be worried about.
Fascinating. Now we're talking about one company's product here. A lot of other companies are working on different applications, but using essentially the same technologies, right? Natural language processing and, um, large, um, language models. What other kinds of things may be coming down the pike?
So I think everybody's aware that, uh, from OpenA- AI, maybe even by the time people hear this, there is going to be elements of what's called GPT four release, which is a model that's much larger than the ChatGPT GPT 3.5 models we've been playing with, and if the capability increases have been at the scale they've been before, that's gonna be a big game changer. Um, Google has by far the largest research team on AI, and they've been cautiously rolling out products and have specialized products in different industries that might happen soon.
And then you're just gonna see all kinds of other explosions outside of just large language models, and there's other companies, like Anthropic has Claude, which you can get access to. So, there's a lot of LLMs that'll be out there soon. There are other large language models, which are these text completion tools, but that's not the only tool out there. So, there are these what are called diffusion-based models, which are often used for creating images. So, I can, you know, give me for DALL-E, for example, and you can just easily create images with text.
You'll be able to do that very soon with video and just describe the video you want to see and get a copy of that video. You can do that for user, uh, interfaces, so I can describe a UI in a couple sentences, and there are tools now that will create that for you. So, there's a lot of specialized tools coming out, ones that are s designed to be companions for meetings, there's ones that will help you write particular kinds of writing.
So, we're seeing an explosion of tools and and fundamental technology that is only growing faster.
That's where the strategy documents need to start coming out from from organizations too, right?
A and experimentation. I mean, I think it's great to have the strategy documents. I think that, you know, the compliance questions are sticky, but, um, I don't know if they're gonna be resolved anytime soon, right? The questions about what's okay to have out on the web versus what isn't, and when can I use this and when not, and what's the legal copyright implications of this, and what are my legal obligations are sort of gonna be unanswered for a while while these technologies are out in the world.
But don't wait for all that to be answered before you get started.
I mean, as, uh, you know, used as ethically and in compliance with the law, obviously, but I mean, I think to not do experimentation because you're waiting for clarity seems like a bad idea. I obviously would not be doing this in cases where danger is involved. You should not be using this as a doctor to be writing notes to patients, right? That would be unethical and wrong. You know, I think you shouldn't be doing this where you're, you know, in where there's federal compliance that you're violating.
But I think that to wait to start experimenting is a mistake.
Ethan, this has been wonderful, thank you so much.
Thank you for having me.
That's Ethan Mollick, associate professor of management at the Wharton School of the University of Pennsylvania, and he wrote the HBR article, ChatGPT is a tipping point for AI. If you like today's episode, we have more podcasts to help you manage yourself, your team, and your organization. Find them at hbr dot org slash podcasts or search HBR in Apple Podcasts, Spotify, or wherever you listen. This episode was produced by Mary Du. We get technical help from Rob Eckhart.
Hannah Bates is our audio production assistant, and Ian Fox is our audio product manager. Thanks for listening to the HBR IdeaCast. We'll be back with a new episode on Tuesday. I'm Kurt Nikish.