Right before AI, I was totally fixated on like everybody should just get cash flow positive and people shouldn't burn money. I've completely inverted again. I don't think margins matter right now.I think delivering agents that work matter and uh and that's the only thing that matters >> because you can just if you can if you deliver agent that works uh in two years from now that agent will cost you know a tenth of what it does today to run. So, just like build agents that work. [music] >> Erin, thank you so ...

What does an agentic uh workflow look like for legal and for Harvey. >> I would think of there's kind of two ends of this spectrum that we think about when we think of building this in the product. One is how do you do the very open-ended agentic tasks.So if you give Harvey any question you would give an associate, can we have some system that kind of looks like deep research that can take that task, create some plan, figure out which tools to use and solve this. And then the other end of the extreme is a l ...

Is data a big moat for RV. >> Yeah, I think there's two types of data. And so when we started the company, I think everyone thought of legal data as case law. >> And we've now partnered with Lexus and I think having access to case law and these other data sets is super valuable.But I don't think that is when we think of like the data mode that we think we're going to have as a company. The data that I think is really valuable that I think now people are realizing is valuable is the RLHF data or the reasonin ...

talk a bit about you know how you think about how much of the capabilities looking forward do you think you're going to rely on from the closed model labs versus how much do you think should come from your internal team >> I think this is still to be seen like our general sense is like >> one we like love openai they're a first investor we still work super closely with them a lot of the motivation for model differentiation is just customer demand and our general sense is we will never pre-train models. I th ...

Company Overview - Superme is building an AI-native professional network [1][2] - The platform creates a profile of users trained on their content (podcasts, blogs, memos, etc) [2] - It allows users to interact with AI profiles to learn and receive personalized advice [2] - Superme also functions as a search engine to find relevant professionals for business questions [2][3] Core Principles - Most knowledge now resides in private contexts [12] - Traditional network graphs have diminished value due to superficial connections [12][16] - Knowledge discovery is shifting from search to agent-based assistance [12] Value Proposition - Superme aims to make access to expert knowledge more scalable [7] - It focuses on indexing functional experts often overlooked by traditional content creation [20] - The platform extracts information from various sources, including private content, with user review [21][22] - Superme allows users to refine their AI profile's answers, improving accuracy and training the AI [23][24] Future Vision (2030) - Superme envisions AI profiles for all industry professionals [25] - These profiles will serve as a way to learn about their expertise and thinking [25] - Agent versions of these profiles could be hired within companies [25] - Superme aims to be the most efficient way to keep up with professionals and relevant topics [26]

AI in Software Engineering - AI models have solved coding, but not software engineering, as production speed is crucial [4] - Building AI to accelerate production is challenging due to reliability requirements and the need for multi-agent orchestration [5][6][7] - Resolve AI focuses on using AI to address the complexities of production systems, which involves more than just code [13] Resolve AI's Solution - Resolve AI provides AI site reliability engineer agents to troubleshoot alerts and incidents [11] - Resolve's agents can understand production systems from code to backend databases, offering a faster solution [11] - Customers are using Resolve AI for "vibe debugging," indicating usage beyond incidents and alerts, leading to increased product usage [12] Talent Acquisition - Resolve AI competes with companies like Meta, OpenAI, and Anthropic for AI engineers [14] - Resolve AI attracts top researchers by offering the opportunity to significantly impact the company and change software engineering [16] Future of Software Engineering - Humans will operate at a higher level of abstraction, with agents handling much of the work [17] - Underlying infrastructure and tools will adapt to be more suitable for agents [17]

AI in Software Engineering - AI models have solved coding, but not software engineering, as production speed and tribal knowledge are key [4] - Building AI to accelerate production is challenging due to reliability requirements and the need for multi-agent orchestration [5][6][7] - Resolve AI focuses on using AI agents to troubleshoot alerts and incidents, acting as an AI site reliability engineer [11] - The company's AI agents can replace significant amounts of work, offering value exceeding coding agents [10] Resolve AI's Business and Technology - Resolve AI was founded to address the problem of increasing data and work for humans caused by existing observability tools [9] - Resolve AI's agents utilize human tools and understand production systems from code to backend databases [11] - Large enterprises are adopting Resolve AI's product in production with success, using it for "vibe debugging" beyond incidents and alerts [12] - Resolve AI differentiates itself by understanding the entire production system, not just code, and extracting knowledge unique to each company [13] Talent Acquisition and Future Vision - Resolve AI competes with major AI labs like Meta, OpenAI, and Anthropic for AI engineering talent [14] - Resolve AI attracts talent by offering the opportunity to have a significant impact on the company and the enterprise software engineering landscape [16] - The future of production engineering involves humans operating at a higher level of abstraction, with agents handling much of the underlying work [17]

What does volume debugging look like in my mind. To perform these kind of tasks like help me understand uh what commit has landed in production or is this feature flag enabled, right. An engineer needs understanding of code but also understanding of production and and production is composed of all of these different tools that each has a silo of data but the tools don't really talk to each other, right.And so it falls upon a human to bring their tribal knowledge and also you know knowledge of how to operate ...

Software Development Efficiency - The primary goal is to accelerate the deployment of software to production [1] - The industry observes that deploying a prototype to production often takes longer than expected due to unforeseen issues [1] - A significant improvement would be achieved if deploying to production becomes as simple as creating the initial prototype [1] Agentic AI Impact - Agentic AI and software engineering aim to improve the speed of software delivery [1] Measurement of Success - The key metric for success is the increased speed at which software can be shipped to a broad audience [1]

Evaluation Analysis - The industry emphasizes the importance of scrutinizing both regressions and improvements in evaluation results [2] - The industry suggests that initial improvements observed during evaluation are often misleading [2] - The industry recommends focusing on refining the scoring function when encountering unexpected evaluation outcomes, rather than immediately altering the agentic system or prompt [1] Debugging and Improvement - The industry advises analyzing specific tests or cases that have worsened compared to previous evaluations to identify potential issues [1] - The industry highlights the need to validate whether observed improvements are genuine or artificial [2] - The industry suggests using fake improvements as opportunities to refine the evaluation function [2]