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AI is the biggest technology shift of our lifetime. This show is about how to profit from it together.
Each week I talk with the founders and CEOs closest to AI and Content, the ones figuring this out in real time.
I’m also building an AI content business myself and share the lessons I learn along the way.
WHAT WE COVER
The Titans -- How companies like OpenAI, Anthropic, Google, Meta, and xAI are moving, and why their decisions matter.
The Incumbents -- How content giants like Disney, News Corp, Universal Music Group, and Reddit are responding to AI, and what it means for creators and publishers.
The Playbook -- Real lessons on AI business models, content strategy, creativity, IP licensing, distribution, and getting paid.
Family & Our Future -- Every episode ends with me asking my guest what AI means for our jobs, our families, and the next generation.
ABOUT YOUR HOST
Rob Kelly has interviewed Steve Jobs and Bill Gates, helped pioneer early web content licensing, and built multiple companies with more than $100 million in total sales. His work has appeared on CNBC, CNN, TIME, and Entrepreneur.
Thanks!
-Rob Kelly
The thing that makes people to go, oh, that's cool, was working with the Ferrari Formula One team. Can Data win a race? Absolutely.
Rob Kelly:Welcome to Media and the Machine. I'm Rob Kelly. This show is about AI and how to profit from it. I believe this is the biggest technology shift of our lifetime. I'm a three time founder and CEO of content driven businesses, and each week, I talk with the people on the inside of AI and content, the ones building this new world and learning what works firsthand.
Rob Kelly:I'm an optimist about AI, but I'm also a realist. So every show ends with me asking our guest what AI means for our jobs, our families, and our future. My guess is Ben Amore, cofounder and CTO of DealSage, a startup focused on making enterprise companies AI native. At DealSage, Ben's already used hundreds of billions of tokens across OpenAI, Google, and Anthropic, and he's bringing to his new company lessons from nearly ten years at Palantir, the AI and data company serving governments and large enterprises that's now worth about $460,000,000,000. Ben worked there as a forward deployed engineer years before that role became commonplace across AI, and he's generous in sharing what he learned there.
Rob Kelly:We get into the ideas that shaped Palantir under CEO Alex Scarpe and chairman Peter Thiel, including Thiel's famous line that competition is for losers. The six or so books Palantir handed all employees, why they might hold 10 plus meetings before a pilot and walk away if there's no clear business outcome, Ben's work with Ferrari's Formula One team, why Silicon Valley dismissed Palantir's mix of software and consulting for years, and how amazingly Palantir went public with only about a 100 clients. We also get into lessons Ben is applying now, where the real AI advantage lives, how companies can accidentally give private data to LLMs, and how he's thinking about AI in school as a father of young kids, including whether to ban it. Please enjoy my conversation with Ben Amour. What's the craziest data problem you worked on at Palantir?
Ben Amor:I I worked on a lot of different, very disparate types of problems. The thing that makes people go, oh, that's cool, was working with the Ferrari Formula one team. We were basically helping them take all of the sensor data that is streaming off the car as it goes around the track during the Grand Prix or during the prep. And in real time, analyze it to try and understand what was going on with the car throughout the race.
Rob Kelly:I'm just curious. How many sensors would be on a Ferrari f one?
Ben Amor:That's a good question. Probably somewhere between, like, three and four hundred. The most important one is the downforce. So downforce is like how much force literally downwards they are managing to create because the more downforce you have, the more friction you have. And so the faster you can take the corners without skidding out.
Ben Amor:And so kind of like, in some ways, the whole goal is to generate as much downforce as possible. The questions they wanted to be able to ask were things like, how does the average downforce that we are seeing on this particular corner of the track compare to the downforce we were getting when we ran this in the qualifying races or when we ran this last year. And then tire wear is one that's actually very hard to measure. Right? That's not a sensor that you can just just look at.
Ben Amor:You kind of have to make estimates. Can you fit a better model to estimate how a tire is going to degrade across a race at a particular race course with a particular tire type because you wanna be able to bring the car in at just the right time. You don't wanna go too late, and then you start not being able to drive as fast. But you don't wanna go too early, and then you lose momentum in the race or the next set of tires is gonna wear out too quickly.
Rob Kelly:Who's seeing the data? Like, and and when and where? Are they seeing it right there in the race?
Ben Amor:Yeah. Literally, of the engineers are looking at the data streaming, and and they're collecting the data. Like, how does this particular sensor on this lap compare to all of that historical data that we have?
Rob Kelly:Where are those engineers during the race? Where do they sit?
Ben Amor:So some of them are at the racetrack and they're actually like sat in the team area, But then the others are back in HQ, so in Maranello, which is where they're based.
Rob Kelly:And they're communicating during the race, like, to the the pit crew and everything?
Ben Amor:Yep. Absolutely. There's an awful lot of communication going on across the whole team.
Rob Kelly:Crazy. Can Data win a race?
Ben Amor:Absolutely. Yes. Formula one is an incredibly data driven environment. Use data throughout the entire process of designing and building the car in the preseason and then modifying the car and updating it and changing and and adapting to the situation throughout the season. It's all data.
Rob Kelly:And then for the next race or next year, will the data be a guide to the upgrade to the vehicle?
Ben Amor:Yeah. Absolutely. I I mean, they rebuild the whole car from scratch.
Rob Kelly:Who gets the old car?
Ben Amor:Well, I don't... Depends whether they've got any left at the end of the season once it's been pretty beat up Fuck out the races.
Rob Kelly:Yeah. My son will sign up for that. What'd you learn from the Palantir founders? Let's start with Alex Scarp, CEO.
Ben Amor:The things that I have taken away from Palantir that I think were built into its DNA from the beginning were, number one, you have to have this sort of relentless focus on what is the outcome that we are trying to achieve or to drive here. Right? Like, we are not a technology company in the sense that we are interested in building technology for technology's sake. And if you just wanna, you know, use cool new tech, then it's not the right place because the question is always, what is the goal? What are we trying to achieve?
Ben Amor:What is the outcome we are trying to drive? And then how do we use the technology to get there? And I think that applies both on the government side where it was always like that was the original purpose of the company was we have an outcome we wanna drive.
Rob Kelly:Can you give an example where it was outcome driven where maybe another company would not have taken that path?
Ben Amor:When we went into work with Ferrari, where we ended up working on this problem of helping them understand all of the historical data, we didn't go in there with that understanding that, like, this is the problem that has been scoped. We went in there with the understanding, like, we could probably do better with our data. And essentially, the first thing that we did was be like, okay, let's give give me the 15 questions that you care about being able to ask, that you cannot ask right now. And so the actual product that we ended up deploying to them essentially didn't really exist before the start of that project, and it was built out in order to enable that. That's very different from like, oh, let's come in and we've like agreed a scope of we're going to deliver this set of capabilities.
Ben Amor:It was literally just like, how can we make your car go faster? What are the questions that you need to ask in order to do that that you can't currently? And then we'll work out how we get there with a solution.
Rob Kelly:Were you ever involved in the presales at Palantir or only once the customer brought on?
Ben Amor:Yes. I did a lot of presales.
Rob Kelly:So how would you handle it in the presales? Like, you're having meetings with some entity, some prospect, and they don't have that business outcome. I mean, would Palantir pass on the opportunity or not take it until it could clarify the business outcome?
Ben Amor:We would not take it until that was clarified because it was a huge opportunity cost for us. Right? Like, you're investing and often our pilot teams would be four, maybe six people over three months and unpaid. So it's a huge investment that you're making on your side. And so you have to be pretty confident that at the end of it, that is going to convert into a paid license.
Ben Amor:And our view was always, well, we can't be confident in that unless we're able to prove in that time that we can really make the money or save the money. And so there needs to be a very clear business problem. You know, we would often have 10 plus meetings before the pilot to talk to all kinds of different people and really try to scope out what is the right thing. And that's why, you know, Palantir is expensive or sort of a... Is is the perception.
Ben Amor:And it's like, yes, because we tended to only work on problems that we'd really scoped out as a big impact because we invested so much time that you couldn't do pilots, you know, problems for 2 and 5,500 k because you'd already spent more than that just in scoping out the pilot project.
Rob Kelly:I'm just so curious because I find Palantir so fascinating. When you first started to see Alex Carr hobnobbing at the White House and with Donald Trump or whoever the leaders were, were you genuinely surprised?
Ben Amor:I wouldn't say surprised at certainly by that.
Rob Kelly:Proud or some other... What's the word? How'd you feel?
Ben Amor:That's a good question.
Rob Kelly:You know, not everyone's ultimate boss is pictured in the White House or working at that level.
Ben Amor:Yeah. I mean, I think the thing that, you know, Palantir has worked now for a very long time with the US government and with other governments on very important, meaningful projects. I'd always kind of seen that there was actually these deep relationships that were often built between leadership, so Alex Karp and Shyam and on the government side, Aky and others with the leadership within the government and then also on the private sector side. Right? Like when we IPO ed, we had something like a 100 clients, which is an insanely low number, really.
Ben Amor:And they were all big contracts. And so it wasn't necessarily like a surprise. Oh, suddenly, that's kind of more public.
Rob Kelly:I'm curious. Did that change your view once you saw that a small volume of clients, like only a 100, which is tiny, might be a record for smallest number of customers going IPO. Did that change just you as a business founder, CEO in terms of how you approached closing customers? Like, small can be better?
Ben Amor:Very much so.
Rob Kelly:Small... Low volume, I should say, can be better?
Ben Amor:Yes. It has. What I loved about working at Palantir was this working closely and tightly alongside a client over a long period of time, building a relationship, and doing really, like, meaningfully impactful work for them. And I think the interesting thing is that AI and and large language models are making it easier to do that at a smaller scale. So because Palantir only had a 100 clients and because it was expensive for us to service those clients because you had to have a team of engineers who are all quite highly paid dedicated to them, only large companies could afford to buy that.
Ben Amor:But what you can do now is you can build solutions and use cases with a much smaller, much leaner team. And so I think that potentially opens up that kind of opportunity to much smaller businesses.
Rob Kelly:I'm just curious. You made me think of this. If you, knowing what you know now, had to take out Palantir using AI, you and I start a new company. How would you do it?
Ben Amor:I don't think I would. I mean, what I've ended up doing is somewhat similar in terms of trying to solve business problems, having a platform that can implement solutions quickly, but working with much smaller customers and clients than they do at a lower price point. And so I guess it's just like focus on a part of the market where they are not focused, which at the moment is definitely, you know, the lower and middle market. I wouldn't go out and try and beat them at selling AI solutions to the Fortune 100 or Fortune 500.
Rob Kelly:You holding on to your stock?
Ben Amor:Some of it. Not all of it, but... Yeah. Some of it.
Rob Kelly:I'm a big fan of how Peter Thiel thinks. He's one of the cofounders of Palantir. Can you just tell me what his impact on Palantir was or what learnings you had from his frameworks and concepts? Was there, like, a required reading of, you know, Zero to One, Peter Thiel's book?
Ben Amor:You were sent Zero to One, obviously, alongside some other books when when...
Rob Kelly:Everyone? Everyone in the company was sent a book?
Ben Amor:They're, like, six books.
Rob Kelly:Who else were the books from besides Peter Thiel's Zero to One? Do you remember any?
Ben Amor:There was one called Improv. The idea there was to... That actually you could take a lot of lessons from how people approached improv and different kind of roles and adopting different personas that would help in a sales context. Getting things done. Mhmm.
Ben Amor:I definitely did adopt quite a lot of the approaches from from getting things done.
Rob Kelly:Which ones?
Ben Amor:I mean, the whole system initially, like, the small task, the two minute tasks, just get them done.
Rob Kelly:That was my number one. I use it every day, think about it. And, yeah, for folks who don't know, can you just describe what the two minute rule is?
Ben Amor:It's pretty simple. It's basically when you get a new task in your... In wherever your in tray is, whatever that is, if you think it's gonna take less than two minutes, just do it straight away, and it is very effective. There was one how to interview users or something like that. It was, like, it was sort of how to basically do user experience research effectively.
Ben Amor:And there was another one, it was about the war on terror. Looming towers, that was sort of about, you know, nine eleven and and some of the failings of the intelligence agencies and so on and and what happened there. And there's maybe one more that I miss remembering, but definitely zero to one was on the list.
Rob Kelly:Is there a major zero to one tip or learning you remembered kinda sticking with you?
Ben Amor:I mean, competition is for losers, I think. Was that the phrase? That was kind of, like, maybe the most memorable one, which is, like, you should try and pick a focus where nobody else... And it kind of requires you to be contrarian because, yeah, if you're not being contrarian, then somebody else agrees with you, and then you're gonna have competition around that. The story of Palantir over twenty years illustrate there was a very, very long time where the markets, certainly, other enterprise software companies, the VC world, thought that what Palantir was doing was not a good idea, right?
Ben Amor:It was like, oh, it's just a consultancy, it's not really a product or this focus on data engineering is not the sexy one or whatever it might be. And I believe that history has proven that that was wrong, that actually you could build a product business with incredibly good margins and cash flow while also having a heavy kind of consulting or deployed engineering team on the ground. That was probably like the contrarian position. There wasn't a lot of competition of people who were both product and consulting. And it turned out that that meant that when, you know, the time was ripe for that combination to explode with AI, Palantir was kind of the only one there at that point.
Rob Kelly:Right. And just to be clear, all the frontier models or at least definitely OpenAI and Anthropic have spent an enormous amount of time and money in the last twelve months or so in investing in sort of a consulting arm to various industries. Right? Is that what you're talking about?
Ben Amor:Yeah. Exactly. And I mean, the... Even the the term for it has become the industry standard term for deployed engineering is the Palantir term for those engineers that would go and work with clients that they've used for twenty plus years. It was my official first job title and nobody had a clue what it meant or thought it sounded, you know, ridiculous.
Ben Amor:But that's almost every single AI company you could possibly imagine now that's been started over the last five years. That is what they hire for and that's what they call it.
Rob Kelly:Can you tell me what it means a forward deployed engineer?
Ben Amor:So forward deployed engineers specifically is a technical person with an engineering background who goes out to a client and uses the product and builds on top of the product to implement solutions, but then who also, and this is important, is technical enough and skilled enough that they can contribute back into the core product itself.
Rob Kelly:So last we chatted, you mentioned that your data is where the AI alpha lives. Can you just share more on that?
Ben Amor:The models are very obviously commoditized. Right? Like, you can buy the same models from the frontier model providers that anybody else can. And so applying those to your particular business in a way that allows you to operate more effectively, that is the problem you need to solve. And doing that generally requires you to have that representation of your business in a digital form that you might call the ontology.
Ben Amor:Right? Like, that was sort of the the idea of, like, building the ontology was actually just capturing all of the information about your business in a connected way such that you could understand it, explore it, and make decisions based on it. And I think the opportunity of large language models is that if you have done that, if you have your data sort of extracted, organized, structured, linked, and intelligible through this sort of semantically organized ontology, then LLMs can be incredibly effective about exploring and understanding that and then proposing or taking actions on top of that data. But the precondition is that, you know, the data is in some intelligible form.
Rob Kelly:You shared with me that AI changes what data is economically worth collecting. What do you mean by that?
Ben Amor:Well, there's always benefit and a cost. And for something to be economic, the benefits need to outweigh the costs. And I think on that, what I mean is that the costs of collecting data and organizing it and structuring it have been dramatically lowered. And so LLMs are incredibly good at taking a multi 100 page document, a PDF, and extracting out the two or three key things that you care about. They can do that incredibly quickly and that now incredibly accurately.
Ben Amor:And so the structuring of data from unstructured sources is just dramatically cheaper than it used to be. We have a client that does sell side investment banking, and they do a lot of work in the telecom space. And so they really care about FCC auctions, what's been sold off at auction, at what price, to which buyers, and so on. And the FCC releases this information, and can go and download PDFs. And previously, they'd have had to have somebody who periodically goes and checks that and pulls down the PDF and looks through it and transcribes all the numbers from one place to another place and kind of builds this database.
Ben Amor:And an agent now can do that trivially. You can set up a schedule. You can go and query the FCC website, pull back any new documents, and extract the information. And it's gonna cost you a couple of cents probably in token costs, and it's gonna do it automatically. And so in that case, they probably thought the information was worth collecting anyway, and so they were doing it, but they were having to invest however many FTEs in it.
Ben Amor:And now they can do it almost for free. An agent can now do that for us. So what can we go and collect that will inform us in whatever decision we're trying to make?
Rob Kelly:I'm curious with DealSage, you're the company you co founded. You know, it's AI software to help companies buy and sell other companies, and I know you do a little bit more on the buy side. I'm curious your take on buying data companies now that you know what you know building this. Companies that own data and content.
Ben Amor:On the one hand, like, data assets are now very valuable to the frontier model providers in order to train their models to be better. And so people with proprietary data assets are sort of, you know, exploring ways they can share that. And that's why you see, you know, Reddit locking up its API and making that much more restrictive because it doesn't want to give all of that data away for free. But on the other side, it's much cheaper to generate these kind of data sets now. And so the ability to use LLMs to pull together data from certainly across any publicly available sources and organize it and structure it is much lower.
Ben Amor:And so you really have to have a uniquely proprietary source of that data that you believe is going to persist over time, certainly from my perspective, to believe that that is going to be really valuable. And I think my guess would be that a lot of people that think they have this really valuable proprietary data set that the frontier model providers are going to be interested in licensing are probably going to be surprised at actually how specific they are around what they want and what is valuable to them. And also I imagine the front end model providers are thinking more and more about how do we just build these same proprietary datasets. I do think that there is extremely valuable data that we still need to collect and generate that will make AI much, much better. And I think particularly on the healthcare and the biology and drug discovery, there's still a huge amount that is unknown and there's still a huge amount of data, valuable data that can be generated there.
Rob Kelly:Are there companies inadvertently giving their proprietary data to train AI when they are using them?
Ben Amor:This is a big big topic, an area of concern, and I am a 100% certain there are lots of companies where people are using versions of JetGPT or Claude where they have not got the flag that turns off model training on their data turned off, and they don't realize that.
Rob Kelly:Gotcha. So in general, if you're using an enterprise version of JatGPT and Clawd or any model, there's usually some built in sort of by default. It's not gonna train the model, but it might retain your data for a while, some sort data retention, as you mentioned, policy. Yep. But on the flip side, if you're using a consumer non enterprise version of ChatGPT or Claude, the defaults are pretty much train the model.
Rob Kelly:Right?
Ben Amor:Exactly. You have to explicitly turn it off, and there will be a lot of people across companies across The US and the world who are just using a consumer version of it and just like copying numbers out of their finance spreadsheet and pasting it into that and then saying, hey, you know, do whatever they need it to do. And I think an even... Definitely something that is a big problem for both companies and individuals is people asking legal questions of the frontier models and it not being a tall attorney client privileged and easily and readily available in discovery. And so, like, that I am sure is incredibly widespread and probably a big, big, big problem.
Rob Kelly:Do you consider yourself an AI optimist, pessimist, or some other descriptor?
Ben Amor:Man, it's such a difficult question to answer because, like, where everything nets out is really hard to determine. What I would say is that I am very optimistic in the sense that I think the potential is so clear and so obvious for how AI can accelerate things that will be good for the world.
Rob Kelly:Is there an example of, like, a vertical or something where you just say this alone makes it worth a lot of these AI headaches?
Ben Amor:I mean, think science is an obvious one. Right? I mean, we talked about the National Clinical Cohort Collaborative, n three c, where we harmonized all this data and then people ran research on top of it. Now having LLMs that can write the code and interpret that data and then write the statistical code, obviously, with the supervision of the scientists and statisticians, we could have implemented five times quicker. You could have got those outcomes.
Ben Amor:I think people having to do sort of drudgery is not something that is good. Right? So people who have to spend a bunch of time transcribing numbers from PDF to a spreadsheet. Right? Like, people shouldn't have to do that.
Ben Amor:That is a waste of people's time. It's a waste of a, you know, intelligent person's life. And so having an LLM that can do that and do it immediately and free you up to do more important things is good. But I do worry mostly at the moment about the potential for AI to be used, LLMs to be used as an offensive weapon in the form of cyber attacks.
Rob Kelly:What's an example of, do you think, a cyber attack that could happen that maybe most people... That that would impact, like, an everyday person, that maybe is just not on the general public's radar?
Ben Amor:I mean, anything you can think of, right, like a hospital. You If you can just take down the power supply for a hospital or just mess with the internal network of a hospital, that is going to be enormously impactful. So that would be sort of on a local but very dramatic scale. And then I think on a larger scale, just any kind of major infrastructure, nuclear power plants, etcetera, etcetera. Obviously, the bigger, bigger risk is on the biology side and the ability to design bioweapons and design viruses that could be very, very dangerous, where I think the risk is a little bit more asymmetric.
Ben Amor:And once you've designed one of those viruses, it's much harder to defend against it than it is to defend against a cyber attack. How to weigh it all up and net the positives and the benefits is hard. I'm pretty optimistic about the potential for robotics. But can we have very, very effective robotics that could we have a big crisis in all kinds of areas of society where we need care to be given, to be delivered physical care. And you don't want to obviously completely replace that with robots, you still want to have people involved.
Ben Amor:But you know, you could service that at a much lower cost and therefore to a much broader set of people who currently can't get it if you could have robots carrying out a certain amount of that work.
Rob Kelly:Do you mean like home care? Taking care of a elderly person?
Ben Amor:Exactly. Yeah. So, you know, I I don't think it's like we need to be sending out armies of of of robots to these people, but we don't have enough people. And we are therefore people who need care in their home are not getting it, and they're... They are experiencing severe hardship as a result.
Ben Amor:But at the end of the day, the people are not getting sent.
Rob Kelly:So I know you've got a couple kids. I think one more on the way. These are single digit age kids. Do you have any energy around AI in schools as your kids start to get into elementary school?
Ben Amor:Yeah. It's a very pertinent topic.
Rob Kelly:Like, what do you think of schools that are just banning it outright?
Ben Amor:I think, like, screens in schools and AI in schools, you need to be sensible and judicious. I am going to be concerned if my kindergarten toddler is spending a lot of time in front of a screen or a lot of time interacting with an AI and not a lot of time interacting with the teacher and the other pupils. But on the other hand, I think I use LLMs and AI as an incredible learning tool. And therefore, I think it's a mistake to say, well, we need to completely get rid of it and deprive it from the learning environment.
Rob Kelly:Do you use AI in front of your young ones now? Kind of your toddlers?
Ben Amor:I do. Yeah. Like often, my my son will often ask me about like a... He's loves trucks and trains, and he'll ask me like, what's that on a toy train? And I don't know the answer of like what the specific piece is.
Ben Amor:And so I will take a picture of it and I will ask Claude or ChatGPT, like, what is this part on the train? And it will tell me the answer. And then like, I'll show it to him and we'll learn something about it. And it would have, you know, I could have looked that up, I guess. I could look up and try and find a picture that pointed at the specific part, but I might not have found it.
Ben Amor:It would have been a lot more difficult. It would have taken a lot longer. So I use it for that kind of thing all the time.
Rob Kelly:Love it. Hey. Thanks a lot for all the time today, Ben.
Ben Amor:It's been a pleasure. Was very nice talking to you. Very interesting conversation. So thank you.
Rob Kelly:Well, this is Media and the Machine. A few things about you and me. If you wanna hear about the next new episode, make sure you hit follow on the show on your podcast app. And if you like something about this episode, the guest, the topic, even one part of the conversation, leave me a rating or review and tell me. That helps me know what you want more of, so I can make the show better for you.
Rob Kelly:And if you wanna go a little deeper, head to mediaandthemachine.com and subscribe to my newsletter. You'll get my favorite highlights from podcast episodes, as well as essays I write about AI. You can also email me directly from there. Recommend a guest. If you make it on the show, I'll give you a shout out.
Rob Kelly:And if this show made you think of someone, please pass it along. That's how people find us, and I'm genuinely grateful. Thanks again, and see you next time.