The management at Fortune 500 is too stodgy. They're not adopting these AI workflows. But what you really have is a baby AGI. It's just because people are too lazy to integrate these systems into work environments that they're not being utilized to their maximum potential. I think this is a very minor part of the reason that AI is not as widely deployed as you might naively expect based on how bullish people are about the AI models of today.
Hello fellow data nerds. My guest today is Dwarkesh Patel. Dwarkesh is the host of The Dwarkesh Podcast where he interviews leading scientists, founders, economists about AI, science, and progress. Before launching his podcast full-time, he studied computer science at UT Austin. Dwarkesh, welcome to World of Danc.
I'm very excited. I'm a huge listener of your podcast, of your writings. I'm a massive massive fan. This is like a fanboy coming out here. And one of the things you've really interviewed just tons and tons of super super smart people. What do you think people are most wrong about?
Depends by subject because there's guests on a wider range of topics, but on AI, I'd say that some of my guests have timelines that are too short. They expect the capabilities of AI to be much more valuable, much sooner than I expect. This is not all my guests, but if I identify the thing I'm most likely to disagree with some of my most recent guests, that would be it.
Interesting. And because you're not only an interviewer about AI, but you're a user of it, right? You're using these tools.
And it's funny like every time I'm using them, I don't know if you're running the same thing, like I run into some sort of limitation. Where I think it can do X, but it can't actually do that yet. Now that X keeps moving so every few months, but for some reason I'm constantly thinking it can do X now when it can't. I don't know if you're running into similar or something similar.
100%. Very similar issues. And that's honestly made me update my views around the actual value of AI models today. I think sometimes people have this attitude and I'm sure you run into this all the time where they think that the reason you know you can look at the revenue of OpenAI and it's on the order of 10 billion ARR. And that's like impressive obviously but it's less than Kohl's or McDonald's right so certainly not AGI. And then the justification people have for this is look, the management at Fortune 500 is too stodgy. They're not adopting these AI workflows. But what you really have is a baby AGI. And it just because people are too lazy to integrate these systems into work environments that they're not being utilized to their maximum potential. I think this is a very minor part of the reason that AI is not as widely deployed as you might naively expect based on how bullish people are about the AI models of today. I think the more important reason is that it's genuinely hard to get humanlike labor out of these models. And I've experienced this myself by trying to build these little LLM workflows for my podcast. I'm sure you have these kinds of things set up as well, right? Like getting it to rewrite transcripts and find clips and so forth. And I've just been thinking about, well, why is it the case that I still have to hire a human for these tasks? Why can't I delegate these language in, language out tasks to language models? And fundamentally, I think the problem is the lack of on the job training. These AI models do have an impressive list of capabilities right out of the box. They'll do a sort of any sort of language task you have like this, they'll do a five out of 10 job at it, which is higher than what a default human might do, but the problem is they don't get better over time. At the end of a session, their whole memory gets purged. And unlike an employee, right, like a human employee will they might not even be useful until like 6 months in. But you're training them. They're building up all this context. They're learning from their failures. They're picking up these small efficiencies and improvements as they practice a task. So this lack of on the job training I think is a huge bottleneck to unlocking the full value of these models. And I don't see an easy way to add that in right now. And but the puck is moving though. Like it is moving, but it feels like my expectations are moving just as fast as the puck or I don't know if you're or what I think should happen or I don't know if I'm just a weird guy or that's like what everyone's feeling when they try to use these tools.
I think it's fair to point out that our expectations keep shifting. And this is a famous famously, you know, the goal post we always keep shifting on AI where every year it can do a thing that we said it couldn't do before, but now we say oh well we're still not there because of this X remaining thing. Now that's a fair objection that you could or that's a fair rebuttal to say look you're shifting the goalpost. But another thing to notice is look if you do if you keep hitting these benchmarks if you do keep hitting we passed a Turing test right nobody noticed we have models which ace math exams that are made for the smartest grad students in these challenging environments. And if we keep passing through these benchmarks while at the same time not unlocking the economic value that is implied by AGI which at the minimum should be able to do white collar work right which is worth tens of trillions of dollars every year in terms of wages paid to white collar workers. If we're not unlocking that while at the same time moving through these benchmarks, then it does make sense to shift the goal post in the sense of asking well what is the remaining bottleneck that we didn't previously identify which explains their lack of usefulness.
And what's your thought like what's going on or what's happening here?
Yeah, I think a big part of it is this continual learning this like lack of on the job training which I think is how humans mainly contribute value. Only part of the value humans bring in work is their raw intellect. The rest is this ability to build up context and interrogate their failures and pick up small efficiencies and improvements as they learn on the job. The other big thing is of course these models are just chat bots right now. And it's just very hard to be useful as a simple like you ask it a question it gives out an answer. That's not how most work happens. And so sooner or later we're going to see models which can actually interact with the computer in the full range of ways that a normal white collar worker can interact with the computer including emails, browser, whatever other applications. And of course it can't do all white collar work until it can at least has a modality to access computer use. Now there's all these interesting questions about then how do you train models are capable of using the computer in this way because you're not just doing pre-training on text the way these models are trained right now. But I think that will be a huge unlock as well. And if you just think I mean you have so much insight into just the elite conversation that's happening amongst people where what do you think is like over represented and what do you think is underrepresented in this elite conversation?
I think people are just really stuck on reasoning or coding or exams which test knowledge. So there was famously this exam this benchmark released last year called humanity's last exam and it was supposed to well what it sounds like it was supposed to be this final benchmark. And why was it supposed to be the final benchmark? Because it asks the hardest PhD questions. And of course, if we can answer PhD questions, then you must be AGI, right? Because that's what the smartest humans can do. But that ignores that most work is just not constituted of answering tough chemistry questions or answering math problems. Most work is just mundane day in day out. The things we take for granted because it comes so easily to us. You know just like executing on a project over the course of a month and backtracking when you notice you're going on a dead end. But which is not available to these models at all because of this the fact that their context cannot accommodate a month-long task because of the fact that they won't learn in the way that humans learn on the job because of the fact that they can't use computers.
Yeah, it's interesting because like if I think of like my math like the I took, you know, math all throughout college, I think the last math class that was useful to me in my daily life today was probably 10th grade. You know, I think after that I don't think I've really used that kind of math even though it's probably important like and maybe it was intellectually interesting like it wasn't useful in my own life. Well, my day job is a podcaster, so the level of math I'm using is even even smaller than that.
Yeah, exactly. Like maybe you want to get some probability and statistics and a few other types of things that are out there, right?
And when you think of like Okay, so one of the things that you're really good at is identifying original thinking and then bringing that out on your podcasts. What if you had to think through like the commonalities of these original thinkers, what is that like what are are there any?
Yeah, I think one thing that I really appreciate and has led to some of my best interviews, the people I have learned the most from is deep empirical grounding. I think it just really matters.
What does empirical mean there? I think sometimes people just have this very like pie in the sky. Here's my grand ideas for how society works or something, you know, like here's the philosopher from the 18th century I read and how it explains everything. And more and more I'm just like I don't think that kind of stuff is that valuable. I think it's more about like read the main economics papers and like look at the big trends or in the case of AI look at just like measure the trends out in terms of how much instead of having some grand take about like what is the nature of intelligence I think that has its place as well but also noticing what are the trends in terms of how much compute scaling every year versus how much can be explained of algorithmic progress and what is the sort of uplift that software developers say they're experiencing. Even in terms of grand historical narratives rather than having some take about like some Rousseauian take or some Hobbesian take about you know is mankind screwed or what is the primordial nature of man just like looking at the population size over time and what has been the growth rates like going back to 10,000 BC and trying to learn from that pattern rather than some abstract first principles hot.
Okay. So, it's really just like looking at the numbers and then maybe updating your beliefs based on how those are changing and stuff or
Yeah, I think otherwise it's so easy to just especially the kind of line of work I do where there's no ultimate check, right? You can say whatever and your theory might be correct, your theory might be wrong. And so it's really easy to get yourself in these loops of grand narratives and I found it very useful when somebody just checks that with no here's the empirical evidence in other fields. Now one common failure mode when you get too much in the empirics is just closing yourself off to evidence in adjacent fields. Which I think is also a bad idea like you're just like if you give me the numbers on AI otherwise I don't want to hear that argument in the context of AI discussions but there's also a way to be grounded in when discussing other adjacent fields so one of my favorite Carl Shulman made this point to me he's an AI forecaster and he made this interesting argument about look how do we explain why scaling up AI models makes them smarter and he noticed that there's been this research at primatology by this researcher Suzana Herculano-Houzel which shows that as you increase the brain size or the brain weight of primates between different primate species from you know lemurs or something to humans their neuron count increases linearly whereas with other kinds of species like rodents that doesn't happen. Okay. Well, that is like a very interesting empirical result which like tells us something about maybe what happened with humans is that we finally landed on a scalable brain architecture that and then we were in a evolutionary niche which like rewarded increases in intelligence. Anyways, that is like a sort of like totally different field that you're learning about AI from, but you're still not you're still have some some like real thing that you're talking about rather than here's what this 19th century philosopher had to say about the will and intelligence.
Okay. Well, no. If you think of like these you know these kind of like big thinkers that are out there, there's this one kind of critique that it's hard to be a big thinker these days because you have to like specialize in a very very narrow PhD.
But then you kind of like meet all these like random people out there like whether it's like Peter Thiel, Marc Andreessen, Tyler Cowen, like and they seem to be really big thinkers. They seem to be thinking like across 50 different fields. And I would put yourself in one of those as one of those people as well who you just have like real diverse interests, real knowledge across many many many things. So are we losing the big thinker? Are we losing the person who can think across things in today's world or are we not?
Yeah, I mean, I think all the people you mentioned are super smart, and I'm fans of all of them, but I think those are great examples because I think those people are especially good in domains where they have like genuine understanding of. And that understanding often comes with like genuine like real tangible real tangible I don't want us to just say numbers because numbers is not what I'm referring to. For example, I think Tyler is like has I mean Tyler is like is he a big thinker? I mean obviously he's a big thinker.
But it's because he's a small thinker in a million different things.
Correct. In any given subject he will be able to go toe-to-toe in terms of look if you think this is why Japan's economy created well how do you explain this other pattern how do you explain this result from this other paper in some sense this is whereas I don't know I feel like some of these other people in that list will just have this like I read this book that was written in the 40s and this explains how the modern world works and I think that's just sort of basically useless and I don't think it really does and if you like really get into the nitty-gritty just like just a bunch of words, right? I don't know if I addressed the crux of your question, but no, one of the things I mean you kind of like I thought when I first got into you I kind of got into you because you were like talking about AI, but you really do have these diverse interests and one of the things I love the most about some of your podcasts were like you have this like whole series with Sarah Payne on Japan and you brought like Ken Rogoff on to talk of Japan like why why is Japan so interesting to you?
Hm. I don't know if it's a central interest of mine, but the reason I find the Rogoff interview is so interesting or his claims about Japan so interesting. So Japan famously had the in the early 90s it had a famous crash. It was actually after World War II obviously Japan got decimated. I think in 1950s or 60s Japan's per capita GDP was like 20% of America's and then by 1989 Japan's GDP per capita at least in like pure nominal terms is 125% of America's which is to say that it went from being this destroyed nation to richer than America on a per capita basis and then it crashes again and it hasn't recovered for the last three decades. The GDP per capita in Japan is now less than 50% of America's which is just like a shocking and then the question is why did that happen? Ken Rogoff who is a former chief economist at the IMF and he was on my podcast and he said this was actually the result of interventions the Japanese government made at the behest of US pressure specifically appreciating their currency and deregulating their financial markets faster than they should have. And this just caused a huge financial crisis. And his claim is that unwinding financial crisis is just a super tricky problem because it's not like a sort of like near-term shock where you can you know like we had the 2008 recession eventually recover supply recovers but in this case you just have so much bad debt that needs to be unraveled. Anyways, a big update for me from that example is just that how bad financial crisis can be that not only are they a short-term pain, but they can permanently alter the growth trajectory of a country. Now, many people have responded to that claim by Rogoff by saying, look, he's underestimating the impact of demographics that Japan is just a much older society. And if you also compare Japan to other countries in East Asia like South Korea, South Korea is obviously wealthier than North Korea or China on a per capita basis. But it's also not doing as well as America. And so this could have to do with the nature of you know high-end electronics was just not the most competitive industry in the 21st century as it was in the late 20th century. So maybe it's more to do with demographics and what industry like what things are exporting. But maybe a bigger issue that or a bigger question I've been thinking about and Japan is just one example of this is how much of the world is simply not doing that well right now. We're in America and America's been doing great for many many decades. And we're just quite used to the stock market like stock market goes up. There's some amount of stability, new businesses are created. There's growth, there's new technology. And as much as we complain, that's been the pattern. Whereas you just like look at different parts of Europe, you look at parts of East Asia, you look at even China now, it just it's just remarkable how much of the world really does seem stuck.
Yeah, it is interesting because like in 2008 Europe is like the GDP of Europe is like roughly the same as the US. And you maybe has more people so per capita it's like slightly lower. And then in that time frame since then the 17 years since then we just see a divergence between the two and is it because maybe they didn't handle the crisis better or I don't even understand why that divergence has happened at such a rate.
I have no idea either. It's I feel that's quite mysterious. They also missed out on the internet wave which I think has been a huge part of why between the internet and between energy abundance with fracking I think has been a big like source of tailwind. I mean that's quite interesting in itself that if not for two or three key inputs like this America would also be in a pretty bad position. Like imagine if fracking was not a thing America would be in a pretty bad position or imagine if big tech didn't end up being a thing. You know our pensions would be in trouble. You subtract that from the S&P returns and it's a very different picture.
And that just may come with like okay if you overregulate too much you might just miss a wave that you don't know about like the fracking wave which could have hit Europe but didn't because of you know a whole bunch of different reasons there.
Yeah, totally. I'd be curious to hear from economists about what they think the main causes of Europe slow down. I mean, one that I hear often is the labor market inflexibility that the fact that it's so hard to hire and fire people really limits the dynamism of those economies. I don't know if you have a theory on
I don't know enough, but my guess is that's not totally it because you can it's easier to hire and fire I believe in Eastern Europe than Western Europe. And so you would just think there'd be more arbit. It could be like in the US where it's like harder to have unions in the south than the north so more jobs move to the south. But it just doesn't seem like it's happening at that rate.
Yeah. And what's also crazy, I think in Italy, this might be wrong, so somebody will let me know, but I think productivity per hour has not increased since the 90s or like some ridiculous amount of time which is crazy to think about given the fact that we've had all this technology in the meantime. We've internet advances in logistics and energy and whatever. And the fact that like the net impact of that is still zero presumably because other things are reducing productivity. This is also a pattern in China. So since 2008 if you just look at aggregate the way you calculate productivity is you just take out you look at output you look at how much the inputs increased capital and labor and then whatever is not accounted by that must be the productivity number. And since 2008 I think China's total factor productivity has also been flat.
Oh really? I didn't know that.
Yeah. Yeah. Which and this one explanation for this is one explanation is that they did a huge stimulus after 2008. So their reaction to the financial crisis was this clearly shows that just market driven growth will lead us to some sort of financial crisis. We need to manage this investment. And so they did a huge investment boom which is now resulting has resulted in these ghost towns as they're called or the huge leverage that the local governments building out these huge infrastructure projects that then are now becoming less and less marginally useful because it wasn't directed by the market. A lot of this investment some of it was it just wasn't as productive. And so the aggregate productivity numbers are actually like surprisingly flat.
I wasn't in the workforce 40 years ago, but I do believe that at least today there are a lot of people, a significant minority of people at least in the US that are negative productivity. And I don't know if that has gone up over time, but I believe it has. I believe there is this weird like in some ways like companies have this bargain with the government and with society where they have these extra folks there and they keep them there and so there's actually a lot of people who are not just zero but they're actually making the company worse. And it's just like hard to see it in the numbers that there's all these people who are way more productive and there's people who are dragging them down.
But what is the sort of market explanation for why wouldn't companies just fire or like why wouldn't the companies that decide to fire just out compete the ones who don't?
I don't know. But I don't totally understand it. But it's you just like even if you go like the most capitalist you which is like the private equity firms and stuff you think they're the most capitalist out there that just want to win and then you actually if you actually interact with firms that are like run by private equity firms like they're not run better usually in my opinion. And it's unclear why like they're still bloated. They still have all this bureaucracy. They still do like all these random things that probably don't add value. They probably add negative value. All these like weird committees internally and stuff and all these weird processes that don't probably help things and just kind of like make people move much much slower. So I'm really not sure why maybe it's just ingrained in people's head.
If everyone's doing it, then it doesn't like really matter to out compete if enough people start
Yeah, I've never worked a real job, so I have like no sort of firsthand experience of what or first time my intuition is that there might actually just be like some surprising amount of value in these people. One because I think markets work and I think it would unless there's some other reason this distortion has created, I think it would be corrected for it. But maybe the other is this the sort of Chesterton's fence kind of thing where even though we don't realize why the fence is there or what these extra people are doing there might be some value that we're now I mean obviously there's sectors in which this is not the case like nonprofits or something right it would be or government I would be very easy to it would be very easy to understand why there's not a direct incentive to have efficient headcount but how many extra workers does McDonald's have or Walmart have or a lot of these firms that employ a lot of people I'd be surprised if it's like I would say more on the white collar than blue collar. I think in blue collar they're actually there's a direct correlation to the output there. Whereas in the white collar, I think it's very mysterious.
So maybe the explanation for this distortion is that everybody knows that say 20% of your employees are net negative value but in white collar work since there's no way to identify who those 20% are you'd be worse off by just indiscriminately firing or firing based on the signals you think you have. And so maybe it's a measurement problem and which rational for you to keep these people on board because the remaining 80% are worthwhile that you can't just randomly get rid of people. I wonder if that could explain you because if you remember like four years ago when Meta started doing all these layoffs they did them very they did a very small percentage at a time. They did many many layoffs, each one very small, like in the single digit percentages. And everyone will tell you that's the wrong way to do it. Everyone says you should cut big and then announce to the team, well, never doing a layoff again because it's so bad for morale.
And maybe the theory is like, well, they just did they couldn't cut that big because they had no idea who to cut.
And so they had to do these scalpels very small over time to actually make it happen. Okay, we're going to remove this layer, middle management, see what happens, and then kind of adjust and then move this other layer and see what happens and adjust, etc.
Um, yeah, interesting. More like liposuction instead of amputation.
Um, yeah, exactly. But if you like enough, then you have the same effect, right? I think it's said of Elon's companies that like Elon's saying is you should cut enough that you have to hire people back. Like this is the same thing of like if you've never missed a plane then you arriving to the airport too early.
Uh um yeah. So, but yeah, potentially just as some sort of like pain threshold that you're not willing to go through.
Yeah. Yeah. Exactly. One of my friends who's an investor has a similar theory about scams, which is like you need to have a relatively decently high scam rate where people scammed you out of your money. Otherwise, you're never going to you're not believing enough in the future.
Interesting. And so the theory is like if you if it's like actually investing in Theranos or FTX, like that's not necessarily a bad thing as long as you're not doing too big of a percentage of your investments in these things, right?
If you spend all your time not doing that, I think FTX might have cost more money to investors, not in the raw investments they made in FTX, but in the risk aversion that that created.
Correct. And not just like normal risk aversion, but like specifically I noticed this is in AI where the big people who have a lot of money are making these bets in wrapper companies like Cursor and Perplexity and so forth, which I think might be fine bets, but are not to me as compelling as because I believe that future AIs will be so much more powerful given these unlocks that are coming down the line with computer use and continual learning. I think that sort of like the things underlying that basic research or the unlocking of those key capabilities rather than building wrappers on top of the existing abilities. I think that building that base layer is more valuable but people are not willing to invest in it as much as it would make sense to me too because
Just like because there's not that many companies that are building like the real underlying layer that's out there. So wouldn't that just like really confine the number of things you can invest in there in the AI world?
Um it would in terms of just if you need to want to invest in a foundation lab, but I think there's going to be well there's the physical compute itself. You know, you could invest in the components that go into building the chips.
Nvidia I think is probably like Nvidia is like priced in but you know SK whatever the the HBMs the
Then I think there's like the other components to training which are not raw compute but which are other companies involved in the training process. I don't know like people who are building data sets or something or have novel techniques there. I guess if I had more ideas, I would stop becoming a podcaster, but I certainly feel like right now just pouring money into because some people the mentality like look, we want to be grounded and therefore we want to see raw user numbers and who has a lot of user numbers? Well, it's just wrapper companies, right? Because wrappers are who have users. Yeah. And then they have inflated valuations which might be merited in the sense that AI is going to be such a big deal that even they will be become they will be worth more but I think it's like relatively less undervalued as compared to the other parts of the stack which don't have a clear like monthly active user type flashing light. The problem is with some of these research things that are out there like their valuations aren't you they may have zero ARR and their valuations are the same as another one that has 100 million ARR and growing
You know 100% year-over-year and so it's hard to know where you should put your money.
No, that's definitely true. Yeah, agreed.
So if one was like super cheap, I would
Yeah, it's great. Yeah. And maybe you should just be giving your money to, you know, Hinton's lab or something like that and you donating it somewhere there, right?
How do you how do you are you an angel investor? How do you think about like that side of things? Like because you have a very unique view of the world like you could you in some ways you could be a great investor. When I asked Tyler, I was like, 'No, I don't invest. I don't I have no desire to do it. I invest in people. I like giving money away to people and seeing what they can do with it, but not to make a financial return for myself.' Hm.
Um, it's also crazy how much Tyler has accelerated so many different ecosystems, but even within AI, which is the one I had visibility on and which is I don't think his primary focus at all, there's many people I know who are doing very interesting things who in some way can source themselves back to Tyler and the initial 20k career grant that he gave them. So, even if he's not I mean I guess if he was in a position to receive the financial dividends from all the surplus value he's created I think he'd be a multi-billionaire.
Well I honestly I think like I mean for most people even when they make billions of dollars they then they just spend it on other things like respectability or other you know status or other types of things. I feel like Tyler has he's gotten that billions of dollars of status and respectability back in some sort of way. Like everyone the whole ecosystem really appreciates him in a way that you know in a way that they maybe wouldn't appreciate him if he was just a billionaire.
Yeah. This reminds me of that this quote that Patrick McKenzie has about being a salary man in Japan where he says look you might be wondering why are Japanese salary men willing to work so hard for so little money and his answer is in other parts of the world you work hard so you get paid a lot and you spend that money to buy status and Japanese conglomerates have figured out a way to cut out the middleman and just pay you in status. Um, but anyways, it's to your original question about Yeah, I've got to figure out something to do with the podcast ad money. So, I once in a while if I'll not like actively doing it, but I have friends who often start or are working on interesting things and maybe like this year I've started seeing if I can be involved in some way, but just if something still falls in my lap basically.
What you said most people are kind of sleeping on like the collective AI advantages like how do we shift our mental map from these smart tools to the smarter let's say ecosystems?
Yeah I think people are underestimating when you think about AI you're just like thinking about the intelligence of an individual copy of model and I think this leads people to ignore the unique advantages that AI have which have nothing to do with the raw intelligence but the fact that they are digital. Obviously humans a lot of the value humans have also comes from the fact that we can cooperate with each other we can form firms and institutions and governments and so forth but this ability to cooperate and coordinate will be so much stronger with AIs. I mean one obvious way is okay suppose this continual learning thing is solved right so suppose AI can actually learn on a job. And my take is that what this
Yeah. And I'm not sure if like memory
Really captures what this is. I think we really don't even have a good mental framework for what it is humans are doing that makes you so much more valuable in six months after you start the job than you are day one. But suppose we get that going in AIs. A human who learns on the job is upskilling in that specific skill in a specific job they're at. Suppose an AI model is widely deployed through the economy and it's learning how to do different jobs across the economy. It can learn from the experience of all of its copies. So a single model is potentially learning how to get better at every single white collar job to begin with. And even if there's no more software progress after this point, right, we're not making like found some new way to train models. That alone is potentially enough for what looks like a broadly deployed intelligence explosion.
This thing is now functionally becoming super intelligence. And that's one of the many ways you could have these sort of like collective advantages because of the fact that they're digital that humans don't have. Yeah, I don't know how we make people more aware of it or I'm not sure even it's I think it's probably hard for even us to sort of anticipate in advance just because predicting the future is very hard what the implications of these collective advantages are. But you can even lower bound it to these kinds of really wild outcomes.
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So many of you think of our listeners of this podcast, World of D, so many of them lead data companies or work at data companies and we haven't yet seen like an explosion of revenues to these data companies from the AI boom. What if you were kind of what would you be thinking about if you were working or leading one of these data companies?
I mean any I mean people have so many different kind of random things that they sell data about some widget. I mean usually data is about four nouns, right? Like either about people, places, products or companies, right? And then they could be crossed with each other. They're sometimes crossed with time and price. Right? So you could have like, you know, data about molecules would be about a product or data about, you know, some person out there and what they're doing or whatever it might be.
