Writer CEO on Generative AI and the Enterprise

How does it feel with everyone trying to surge in on the enterprise opportunity here? How are you standing out and making sure that you keep the keys like Ubers and clients that you already have? Yeah, it's it's actually really exciting to see all of the investment, right. We've been working on this, my Co founder and I for 10 years previously in a machine translation startup. And so to see all of this attention is, is actually amazing. But the way we stand out I think is with a really differentiated platform that helps enterprises with the last mile which is 90% of the work in AI. May you've been on the show a number of times over the last two years or so. And each time I always reflect on sort of the the the rate of change for the industry. It also grow for your company. Clem de Long of Hugging Face just gave us some numbers about the sort of size and scope of of how they're doing, He said close to profit or near profit or something like that. But just just tell us about your company and how it's doing. Yeah, I mean it's been an incredible rate of change. When we started the company, we knew AI was going to be better at people at reading and writing, and that has certainly happened. We now say if you can write it, you can build it because AI is not just the technology, it's the way to build new technology. But building AI apps is actually still quite difficult. And so the rate of change of of just what we've been able to do, I mean it's hundreds of enterprise customers, hundreds of thousands of users, thousands of applications that are in production. So a lot of this kind of question around like how you get applications from POC to scale, you know we've been doing that for years now and it's just had a tremendous impact on the growth of the business. You have some relatively new work on, on models at Writer. Tell us about the kind of the latest and greatest on the tech side of your offering. So over the past few months we've introduced vision as a capability into the platform. We've launched Palmyra in 32 languages at really, really high quality beating human benchmarks, our customers tell us and Next up for us are large reasoning models. So software that writes software which we're really excited about being able to go from, you know, work substitution to real work reinvention and orchestration using AI. At the very start. You said basically 90% of the work isn't just getting the right language, large language model in the door, but it's implementing it. It's all the other bells and whistles that go to ensure that you get operational efficiencies that you put it to your own work flows. What are some of the best ways you're seeing it being harnessed? What are some of the worst ways? Because everyone's still waiting for this Eureka moment where all of our exuberance around AI actually makes a real difference around 100%. There are 1500 LLMS, right? If large lengths 100. Yeah, I mean, and they can pass the MCAT and the LSAT. So if LLMS were the answer, everyone would have the generative AI program of their dreams, right? But that's not the case. There's so much work to get the data and the context and the workflow from the business user into the application, right. And that's what I what our platform does. It's this collaborative interface that combines the LLM with all of those building blocks. And that's that's where the magic is because the LLMS themselves need so much more context about the business to be able to do what customers need them to do. You've said before that basically large language models are going to be commoditized. The foundational models are going to be commoditized, particularly from a consumer perspective. Where then does the value ultimately end up lying? Because there are so many people trying to fix problems using generative AIA. Lot of them are coming to you to try and be bought or helped at the moment, I assume because they're running out of money themselves. Yeah, there's there's certainly a lot of air being sucked out of the room by big tech, right? But there's still a ton of opportunity for start-ups. Microsoft has to build for the lowest common denominator, right? So individual productivity is very different than team productivity and team workflows. So even though it feels like we're going going to go through sort of a big consolidation phase, I do think there's still a ton of opportunity for for startups. We have made a small acquisition that we'll announce soon and I think we'll make others. So there certainly is a real high barrier for entry to come in and serve the enterprise, but it's still there's so much blank white open space for start-ups to, to help enterprises compete. It's interesting maybe that you use the C word consolidation. I don't think Clem Delong went as far as using the word consolidation, but I think you know you you said something a moment ago about big tech sucking the oxygen out of the room. It goes to the open source closed debate. I assume you sit on the open source side, but but just weigh in. So we're kind of in the middle. So our models are proprietary, a bunch are on on hugging face. So later generations of models, but our latest models are are closed source. But by being in the middle, what enterprises really need is the ability to audit, right, and have the transparency around training, data and all sorts of things related to the models they don't really want Like the last mile cumbersomeness of of necessarily like fine tuning or running the models themselves is, is what we're finding. And so like in the in the kind of sucking the air out of the room, the confusion around what vendors to turn to and how to actually get great applications shipped, that's where that's where I think there's still a lot of confusion in in the enterprise. And I think there's still all that work to be done to minimize hallucinations to ensure that we're seeing a clarity of where the underlying data is coming from and you're not having copyright issues. Give us clarity on your business now. Have you been approached to be bought? Are you remaining independent? Are you raising more money? So there's there's a really long, I think, a product journey for us to really realize our vision. So I'm really excited about remaining independent. It used to be a year ago that I would say, you know, LLMS are for the drudgery, the work you don't want to do. Today, the capabilities are so incredible. They're as good as us. But the future is work where you get to do the work you want to do and LLMS do the rest, right? Because one person's drudgery is another person's creative passion. And that's kind of compelling vision for the future of work. We're not seeing enterprises come up with yet. We talked to hundreds of companies a week and that really feels missing right now, kind of executives painting a vision for what AI looks like inside their companies in a way that brings people along. So there's there's a lot to do both in you know, kind of bringing our vision into the world and and helping companies achieve theirs.