AI and the Future of Work: Artificial Intelligence in the Workplace, Business, Ethics, HR, and IT for AI Enthusiasts, Leaders and Academics
00:28:55 · Rahul Vohra
I remember when when I was first coming up, there was basically nothing online about how convertible notes work. The safe hadn't been invented yet, just just to pick a a random example. And so trying to figure these things out from first principles working directly with lawyers was very slow. And number four, getting early traction. You know, just a few years ago, a good growth rate for a YC company was five to 7% per week. 10% per week was exceptional. Whereas last year, the average growth rate for a YC company was 10% per week, with plenty doing 15 to 20% per week. And it's not uncommon nowadays for a Y Combinator company to get to a $10,000,000 of ARR in their first year. That used to be completely unthinkable. That was like once in a blue moon. Now, again, it's not uncommon. But some things are harder. Taste, for example. When anyone can build anything cheaply, the real skills actually become taste and restraint. And it's an old example. But one of my favorite examples of this is actually Instagram. And maybe you'll remember this. Before Instagram was Instagram, it was actually an app called Burbn, and it was a lot more than just photos. It was actually in that era of applications that I think we called social mobile local, SOMOLO or SOLOMO. It was this this weird acronym that we had, and then there was like a hyper social mobile local set of apps. And it did... It was a social network. It did check ins. It did location finding. It was like, where are my friends on a map? And it also did photos. And it didn't really take off. It didn't take off because it didn't have a point of view. It didn't do one thing incredibly well. It didn't channel taste. And when they narrowed down to the photography piece and did all of the things that had been very well documented, that's really when it blew up. And that... That's a really good simple example of taste actually making the difference. The other thing that's harder is competition. There are a lot more smarter people and more venture dollars now going after company building than there ever were before. And then the third thing that I think is actually harder is distribution. And in a world where building products is increasingly easy, it becomes increasingly important to figure out how to get those products in front of users. And in such a world, leverage actually shifts a little bit back to incumbents.
00:31:20 · Rahul Vohra
Startups need to find their edge of a wedge and then a durable business before incumbents leverage their distribution to do the same. Basically, before incumbents build or buy the same thing and put it in front of all of their users. So I would say in many ways, it's the same. Pick gigantic markets, either those that are big today that are going to be big in the very near future. But ultimately, it's never been better to build a company. We have never been in a better time. And the proof is in the pudding. Companies today are growing bigger than ever, faster than ever. The bar to be one of those companies might be harder than ever, but the prize has never been bigger.
00:32:01 · Dan Turchin
[00:32:01] S1: On this podcast, we talk a lot about the complicated relationship between humans and machines. And unlike, say, getting through your email inbox faster, AI is forcing us to wrestle with some more complicated questions, like existential ones. Like, literally, like, why am I here? Because it's feeling like this artificial entity does a lot of what I thought was uniquely part of the human condition better than I can. I know you're a deep thinker, so I I get to ask you this question. I haven't heard heard you talk about kind of your perspective on the incursion of AI into in in the... Into everything we do. What's your thought about how does how does Teamhuman... How do we as humans achieve a balanced relationship with AI? It gets us beyond the current state where it's a very polarizing topic. In general, particularly in The United States, as you all know, there's a great mistrust of AI, and yet we're increasingly dependent on it. How do you think about those complicated topics that... Both at a at a Rahul level, but then at a macro level? I think that
00:33:12 · Rahul Vohra
[00:33:12] S0: AI is going to deliver so much improvements to our quality of lives and the economy and our national security and America, as as well as the world in general, we are only beginning to scratch the surface with what we're seeing today. At the same time, we're also... And this is me as as a person and also us as a company, where I am very aware and we are very aware of of the anxiety and the fear and the real impact in some cases that it's having, on people's lives. And we think that what we can do is is build AI in the most responsible fashion, and I I can talk about what that means, to us if that's interesting. But I'll I'll say at a high level just for the moment in terms of the the the overall impact and and and why I'm excited. So maybe starting with the name, Superhuman. Like, why did we rename the whole company to be from Grammarly to Superhuman? And as you can imagine, that's a complicated choice. It's difficult. Like, we have to change websites. Superhuman is known for being an email app, not not the whole suite. So there's a lot of work. In some cases, it's an uphill battle to make that work. But the reason we did it is not only does Superhuman stand for what we're trying to do, which is to give you superpowers everywhere you work, but it also has human right there in the name, front and center. The way that we wanna build AI is by allowing you and me and humans to do what we uniquely can do, be our most creative, our most strategic, and our most impactful. And as amazing as AI gets, there's something special when you and I get in a room together or we get on a call and my unique lived experience combines with your unique lived experience, and there's a certain frisson there, coming together of, decades of of somebody's, in this case, two lives. And, obviously the combinatorial explosion when you have a third person or a fourth person is is kind of mind boggling, that AI simply will never be able to do, or at least not in the foreseeable future. Like, theoretically, if it recorded everything that happened in my life and everything that happened in your life, maybe. But that that is that is not gonna be a problem for many decades. So there is something still very special and very unique about teams of people coming together to combine their lived experiences, and that's Superhuman.
00:35:47 · Rahul Vohra
And that's one of the things that that we're excited to to deliver. Then as I think more broadly about the overall economy and and the impact on on jobs and and so forth, I'm sure that other guests have have spoken about Jevons paradox, but but, I'll I'll redo it if if they haven't. I I think it's very germane, and more more people need to understand this. So Jevons paradox came from the industrial revolution in The United Kingdom way back when. And, of course, back then, all power was, derived from burning coal. We'd we'd burn coal to create heat, to heat up water, to create steam, to move engines, and and that... That's how power used to be generated. Right? And then James Watt came up with a much more improved steam engine that made the unit... The the energy extraction from coal way more efficient. And there's a lot of analogies between then and now in AI. Back then, there was this widespread panic that the demand for coal would go down because we need far less of it, and therefore, the coal industry would fall apart. And the coal industry was was all of everything back then. Everything kind of rode upon it, and that therefore the whole economy would fall apart, there'd be widespread unemployment, and and everything would be terrible. But it turns out the exact opposite happened. Total coal consumption surged as engines became cheaper to run and were adopted much more widely for a greater set of use cases before as new things became profitable that previously could not have been imagined. Let's do a more recent example. So computers and digital tools, if you go back to, like, nineteen sixty, seventy, eighty, ninety, There was actually a panic at that time from paper companies and lumber companies saying, oh, as we digitize everything, like, the the the paper industry is gonna gonna fall over, or it's gonna die. But actually, turned out that global paper consumption tripled during that period because printers and printing became easier and cheaper. When spreadsheets, when Lotus one two three and and Excel started to become really popular, people were really worried that, the accounting industry would go away. And actually, the opposite happened. It it led to a whole new line of work, which is FP and A. Now as we catch up to today, a lot of people have said, well, with coding agents and software factories, does that mean we need...
00:38:25 · Rahul Vohra
Doesn't that mean we need fewer software engineers? And and hopefully, you can see the pattern here. This is exactly the same pattern of argument. Actually, it turns out you need way more software engineers because way more kinds of software are now more profitable. I had a really great conversation with a a not for profit founder the other day who is, building technology for for certain parts of Africa, and she was unable to do so profitably. Profit's the wrong word. She was unable to do so in a way that was sustainable before coding agents because it was just too expensive to afford to pay, everyone to do all the things. Whereas now she can create the software and hire the software engineers, and deliver it to market much more cheaply, thus enabling a whole new category of industry that didn't exist before. And in in this case, it's it's a really great not for profit story. So it turns out there's way more demand now for software engineers than there ever was before because there's so much more software to be written. And and by the way, when you have more software to be written, you have more security risks, more complexity. So there's more roles in cybersecurity and compliance and governance. I'm hearing the same thing in legal work, by the way. Folks have said, oh, aren't we gonna need far fewer lawyers now that you have Harvey and the Gora and and so many other of these these legal companies that are automating so much legal work? Well, turns out that when legal work becomes really easy, the demand for legal work increases. Like, a lot more cases, get brought. There's there's a lot more demand just to to do things, and so, case volume has massively exploded. I was talking to a lawyer whose firm has now like 10 x the case volume they used to have. And when you have 10 x the case volume, sure, AI is gonna do a whole bunch of that. But it turns out the set of stuff that you need humans to do also 10 x's. And so they're actually hiring more lawyers than ever before. And most surprising example of this that I came across was customer support hiring, which you you would have thought of all the things, surely that's where AI is gonna have the biggest impact on jobs. Well, it turns out customer support hiring has recently rebounded and is now growing 10 to 15% faster than other sectors, which I I think is is remarkable.