BUILD PLATFORMS

“So what in the past, what used to be thought of as non functional requirements such as latency today is product feature. It is it is baked into the experience of a developer. So these are some things that, you know, we are seeing a paradigm shift in terms of what we need to bring to the fore to the developer experience to keep in mind when you're when you're implementing your systems.”

“One is your actual business and the product or service that you're selling, and the other is your instrument. So the CEO or the CFO of a publicly traded company spends a lot of time on their business, on their internals, all that kind of stuff. But they also spend a lot of time selling their other product, their stock, to their investors. And the honest truth is that you just have to do both of those things.”

“All the more important that observability comes to the forefront in stochastic systems like a multigenic application. All the more important for us to be able to replay agentic actions and try to understand how it function. Agent behavior needs observability along many different dimensions in terms of what are the tools involved, what was the reasoning mechanism that led to that tool invocation. And, overall, what was this context that passed across systems?”

“Net net, what can you take away from all of this? It's not just a revenue story. It's not just build great products, generate tons of revenue. We're actually doing that and the prices still aren't going up. So what it tells you is there's a trust problem. Investors have stopped. They never really understood what the difference was between a token and equity in the project.”

“So, I think, customers that are new on the multi agentic journey are one of our more naive users. So we have to offer them prebuilt blueprints that they take and run for their use cases. There are savvy customers who know what they want to do, and you offer them those developer kits. So we have all of these different ranges that we offer to the to our customers depending on where they come from, what their use case is, and how can they get to the fastest path to production in the least, constrained manner as possible.”

“We moved from a classic ML world to a world where we have LLMs, generating responses. And now we want to move on to a world where actions need to be taken, specific goal oriented actions need to be taken. And when the problem that we are working on is a complex one, with multifaceted, aspects associated with it, That's where multiagentic comes into place. So, basically, we have a large complex goal which we have to break down into specific steps, and each step is basically narrowed to a specific agent.”

“Chat Concierge for us was, our beachhead initiative around deploying multi agentic deploying a multi agentic solution. So, Chet concierge is essentially a a auto deal dealership, project or application that was deployed out to our auto dealers to basically bridge that experience between dealers and their customers and make it very seamless. And we need to understand we are moving to a world where the car buying experience doesn't start at the dealership. It starts before. It starts when they go to their website and try to figure out, okay, what's the inventory?”

“But really, what is happening here is that capital markets are getting rebuilt and they're getting rebuilt on chain. That is the big opportunity mega trend of the next five to 10 years. And so when you think about capital markets, what powers capital markets is trust and infrastructure that connects investors and people with capital, the people that want to use that capital businesses, on-chain businesses. So that is the opportunity that we're orienting Blockworks around.”

“By the way, all of that whole data layer is powered by AI, right? So the days of, I used to be a consultant, any analyst knows the pain of like sorting through two and a half year old 10Ks, and you're looking for one line item, all of that is going to be answered by AI. Like, we can do this stuff way, the exciting thing about this is that we can actually do this much better than TradFi does.”

“So we actually went the opposite way, and we went down into the bedrock and the foundation. And if you look at a lot of our branding, we're keeping that signature Blockworks purple, but we have a lot of imagery of stone and solidity and caves and identifying things via echolocation. And that's the reason why I think this is so cool. And it informs what we view as the opportunity for this space, which is, for lack of a better word, it's the seam.”

“We used to, hey, crypto, we're in a bear market because FTX blew up. We're in a bear market because Gary Gensler. We're in a market because Jane Street is selling. I mean, it's just hilarious to me that now we're blaming Jane Street. I mean, if you think of the villains list, like how low the bar has gotten. But now I think we're realizing there's no one to blame but us.”

“When you, you are the most successful when you can offer two things, reasoning and specialization. So reasoning capabilities with our agentic platforms, a agentic frameworks in the platform, we are bringing that to the fore. Specialization is something that is very, very crucial. So this can be achieved primarily using, specialized models and fine tuning. Student teachers student distillation gives you that control you need to have on, providing personalized experiences as well as providing, having some control over your latency metrics.”

“For example, you know, 90% of our customer service cases now are managed by agents. And, you know, that that means, like, only 10% are actually involving people. And so there's a lifting and a shifting and a changing of the guard in terms of what people do for the critical thinking and the judgment calls that they have to make instead of the tactical, you know, work of just grinding out details.”

“At the end of an interview, he said to me, Bill, it's a very interesting interview. You're interesting guy. The HR department's gonna get in touch with you in the next couple of weeks. And I said to him, you know, mister Fuller, I don't think you completely understand the situation, sir. And he looks at me with a tilted head, like, what's this kid up to? And I said, I haven't broken a promise to my father in twenty one years, and I guaranteed I won't come home tonight with my employee badge in my pocket. And he goes, you know, Bill McDermott, as long as you haven't committed any crimes, you're hired.”

“You know, you've seen what we did with Moveworks, the agentic front door to the enterprise, Inveso, which is human and nonhuman identity, and now the security move with Armis on top of an unbelievable core. By the way, we integrated these businesses in twenty days. Twenty days. So a lot of companies don't have the engineering power to say they can do hard things quickly.”

“I think the core is that, you know, you really need to treat agentic AI as a system. It's truly a system. You have to start with governed data. You have to kind of put in that risk controls baked into multiple layers of your, of your application or your system. You have to look at, latency as something that needs to be optimized end to end. And, understanding that your biggest gains do come from postproduction telemetry is also critical.”

“If an agent can do it as good or better, that's an easily easy economic decision to make, which is why, you know, there'll be 2,200,000,000 of these agents entering the workforce in the next couple of years. So, you know, there's gonna be a lot more agents than there will be people.”

“You know, this is the moment when leaders really matter. Because as the waters get choppy, you know, we see who's tough and who's not. And when the tide goes out, you wanna be fully dressed. And even if things don't look so good, you'll figure it out if you're fully dressed and you're ready for the battle.”

“And then you have to have a position that you take that brings something unique and variable to the equation. I always tell people, if two people are in the same room at the same time with the same opinion, one of them is redundant.”

“I bought the business for 5,500 notes, 7,000 with interest. If I make the payments, I keep it. If I miss a payment, they take everything away from me. What was really interesting about the story is I didn't have any money. But what I did have, because I worked there, is relationships with all the suppliers. So I got them to give me the first order on consignment. I said, I will always pay you back. This is not a favor. This is a chance for you to get your shelves filled up with your stuff that I'm gonna sell for you.”

“Right now, I think it's just tell me what I need to know. It's not like, let me come in and discover your problems. Well, let me bring you a solution because I have one in my pocket, and I can't wait to share it with you. It's like, you know my business. If you don't know my business, there's no conversation. But let's assume you know the business. Be very prescriptive.”

“Let's take that cost, and then let's take the cost associated with the human capital doing that instead of something else because the platform was doing the work for you. And then let's add up the cost of the GPU factory, the business model associated with the language model company, and the tokens that will materially affect their business model. We've actually done the math on this. And so for a simple application on our platform, it would be 10 times greater in cost to try to replicate it with a language model.”

“If you're not following along on your screen here, we've gone from about 2 million some tokens to just over 35 That is an explosion of the supply, right? So if you were to look at the normalized market cap and look at it on a per-token basis adjusted for the supply, it's this blue line here, which is way worse, which is basically, as crazy as this sounds, the average token is essentially where it was in July of 2020, pre-two bull markets ago.”

“I mean, I think everyone overcomplicated, and something like the four-year cycle is clearly in effect here. And if you look at how this has played out in the past, there's the initial dump, then there's actually an initial recovery faster than you think, that no one really believes, because it doesn't translate business sentiment and activity lags price by about six months.”

“Now the other thing about that is if I give you the language model and it makes a mistake, you call me up and you say, hey, Bill. The language model made a mistake. And I say to you, the language model works. And you say, yeah. It works, but it made a mistake. Well, it's probably right, but it's not deterministic. And what we're learning too is people that run businesses understand that people make mistakes. They never will forgive software for making a mistake.”

“I'm thinking about an example that we, you know, we've all heard about. I forget the the very specific scenario, but, a business had a chatbot. I think it happened in Canada, had a chatbot on their website, and the customer asked for a discount, and the the chatbot basically gave them a discount. You know, this would clearly be disastrous in a, in a car dealer type of scenario. Like, how do you, make, you know, generative AI and agents safe, you know, for, you know, these dealers that have a lot at stake?”

“So what this actually is, is a kind of ideological, political movement, new technology that moved from the fringe into winning. This is literally what winning feels like. But now some of the parts of our ideology that hasn't survived contact with the real world are going to have to get changed. And that is the moment that we're currently in.”

“The days of tokens, just being able to launch this liquid instrument, not really define what it is, provide no disclosures about ownership, sales, those days are ending. And by the way, they should end. This does not work. Refer back to the data that we just showed. It's broken. It doesn't work. So there's actually a layer of disclosures that is the bedrock, which is just super table stakes, basic stuff.”