The Ledger, every show

“I, for instance, don't think that the problem two years or five years from now of agents will be relevance, per se, as in the quality of the models. The problem will be very boring stuff, such as, is it the right data, or have we actually updated the data for …”

“Generative AI, as it is built today, is not meant to provide any consistency in terms of how statistically you reach a business goal through the output or through your predictions.”

“You will still always need as an organization to be able to build those models because there are actual business decisions in them. Like actual risk profile with conscious decision in it.”

“Our vision is that enterprise at large will not have one agent, but like more, 500 or 1000 of them.”

“All of those capabilities could be startups by themselves, but the same way we thought about the platform required for the enterprise back in the day, 10 years ago, we think that for the world of LLM, all of those capabilities will just become features of plat…”

“It's actually fairly realistic for an enterprise to have hundreds Of Generative AI applications in production at some point.”

“And so I sympathize with, like, everyone that actually needs and have some domain expertise, but won't get into code anytime soon, even with Copilot or whatsoever, and that still need to leverage their data in order to get things done.”

“We see quite a bit of usage patterns where people would start GPT-IV for design and then decide to move to Essentially something cheaper, like nixtral self-hosted or nixtral self-hosted when moving to production.”

“I think that in the next five to 10 years, it will be actually Universally mainstream, as in most companies, a very vast majority of companies will have hundreds, if not thousands of automated systems that automate ordering and some form of cash management and…”

“There is no way for the enterprise in the long term, if you move forward and put yourself from a, if you go forward by 10 years, that you have like five, six, seven, eight different tools that you use to manage the data life cycle. It will make no sense.”

“I think a challenge would be in the future to have, like specialized people for operations on data that will need to understand well scalability issues from a system administrator perspective, but also what F-one score is, because they will need to be, ah, sma…”

“You had a need for the enterprise to do more in terms of data science, An ML, but a scale gap, because there was not enough data scientists on planet Earth in order to fill that gap.”

“What we will build are agents that are combining those models or those models together in order to do more autonomous tasks end to end.”

“Then enterprise, in order to differentiate and be, transform themselves, will also need to build their own agents. On everything which is about stitching together the data from one vendor to the other, or the overall process, or the thing they actually built, …”

“And back then, we crossed 600 customers. We were two thirty million of IRR. Back then, and we have more than 1000 employees.”

“Because of the evolution of technologies and LLM out there, it's very likely that you will have to switch from one provider to the other over the course of your application, if your application is successful.”

“That Taiku is 10 years old as a company. And in terms of numbers, we are at north of 1200 employees and we ended up last year north of two hundred million of revenue of annual recurring revenue.”

“I think that many companies will have to adopt this kind of systems a little bit ahead of time in order to manage some of the internal expectation in terms of control of AI or expectation on their customers, but also in order to be more ready when the complian…”

“I think that there would be, there is a future of, let's say, Enterprise software where you've got system of exist mix of visual and text and potentially coding representation of things where you can navigate between code and clicking and talking to your compu…”

“I think that what I see as very interesting with generative AI is that it's like a new Lego block enabling a new generation of hackers for many vertical applications. And there are like many, many, many, many vertical applications that can be built using and b…”

“It's probably a 20, 25 years journey, and we are like one third into it.”

“Meaning they won't spend two hundred millions or five hundred millions in order to build from scratch a data and AI platform just because it's not realistic.”

“Meaning a few years ago doing all those EV lifting of being able to do ML on a few gigabytes of data was possibly taking a few days to any seasoned data engineers. Today, you can actually do the same kind of achievement in a few clicks”

“Credo number three, two, actually, is that I think that, well, machine learning is to be automated in the next few years.”

“So I guess that it will also get to global manufacturing in the next 10 years, probably.”

“So it might be that in the following decade, about, where everything will be about having intelligent connected devices, a company that will win will be companies that will be very good at putting predictive models in production, putting intelligence in produc…”

“And very smart people. I think you're like in a group of 50 where the top 10 people there are probably expecting to get a field medal in their lifetime and have a decent sense of it.”

“Yeah, 2030, 40 good startups actually came up out of out of it”

“Every data science story I heard in the last few years actually started with someone.”