Ruofei (Bruce) Zhang

A 12-month professional, voluntary collaboration has finally borne fruit. This morning, I was honored to see the University of Washington News (https://lnkd.in/gRRpDqjA) and the UW Milgard School of Business (https://lnkd.in/gKJ_pwbm) highlight our Responsible GenAI Framework. Why do we care about responsible generative AI? Because GenAI systems are no longer experimental curiosities. They shape decisions, influence behavior, generate content at scale, and increasingly affect trust, fairness, privacy, and accountability in organizations and society. Without intentional design and governance, these systems can amplify bias, create opaque risks, and erode confidence rather than enable innovation. This framework is about making GenAI useful, ethical, and sustainable at the same time. It emphasizes responsibility not as a constraint, but as an enabler of long-term value, credibility, and impact. I am grateful to the collaborators, reviewers, and institutions who supported this work, and excited to see responsible GenAI move from aspiration to practice. To learn more: - Read the RGAF Framework (PDF): RGAF Framework https://lnkd.in/gTaxnndV #GenAI #ResponsibleGenAI International Society of Service Innovation Professionals (ISSIP) Milgard CBA

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#1 - Toss a ball to a friend that is running at an angle and have them catch it. Not that hard for humans in general. Very hard for AI. #2 - Document and explain the math behind #1, with variants for wind speed, elevation, etc. Very hard for humans, relatively trivial for AI.  There's a lot of opportunity at the jagged frontier.

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Barbarians at the Gate (https://lnkd.in/gkEBX79s) is a very interesting new paper from some great folks at Berkeley, with some exciting results showing the potential for AI in systems research. But I think the authors aren't quite asking the hardest problem about where this takes systems as a field. I wrote a new blog post about it: https://lnkd.in/gxS3s4v4 The core point of the paper is that AI can be effective at automatically driving the 'number go up' part of systems research. Given an evaluator, AI can automate progress on implementation. I believe this is 100% correct. The paper also talks about how more focus will have to go into developing evaluators. Again, I 100% agree, but think they're understating their case. Building evaluators (basically tests) is, I believe, going to become the focus of systems and software over the next decade. Which leaves researchers with the hard problem of choosing which problems to go after. That's never been easy in academic systems, and is only going to become more of a challenge. But the best systems research is driven by choosing problems well! This vision for AI could lead to lots more of the least interesting systems research. The "number go up" kind. Which could make it harder than ever to identify the truly innovative and visionary work. We're already seeing that elsewhere in science. So, mostly, I think the paper is right, but not yet asking the biggest question: what should systems research be?

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A thoughtful piece by our own Brian de Luna on Applied Science in marketplaces, and the unique journey we’ve taken at Faire.

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Every research has a story. The published note rarely tells it. Most of my previous research started with an answer already in my head. This one did NOT. When I started exploring how Shared API Services fit into the age of #agentic AI, I hit a wall. The conventional wisdom says: build #APIs, enable reuse, accelerate delivery. But the more I dug in, the more I realized the challenge is not purely technical. Technology is part of the answer, but so are design best practices, people, and team structure. So, some customer inquiries from organizations facing this challenge every day made a huge difference. Those conversations changed my perspective. Thank you for your trust. The #Gartner research is now out: Unlock Agentic AI by Evolving Shared API Services with MCP (Model Context Protocol). Shared API Services were designed with a single consumer in mind. Agentic AI is fundamentally different. It does not read documentation. It does not ask for help. It must discover, interpret, and orchestrate capabilities on its own. That single shift changes almost every assumption behind how shared services are scoped, owned, and governed today. If your organization is scaling AI beyond isolated pilots, this note is for you. Is your team already rethinking how shared services are structured for agentic AI? I'd love to hear what you are seeing. 👉 Link in the comments. A huge thank you to Steve Schwent, who started this research and built on ​Anne Thomas​ key insights around Shared API Services. Thank you, Charles Smulders, Mike Gilpin, Tigran Egiazarov, Keith Guttridge, Andrew Humphreys, and Bhagyanshi Pathak, for all your contributions to make this research better. #AgenticAI #MCP #API #SoftwareEngineering #PlatformEngineering

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To FTP or to sFTP? Been using these technologies for 30+ years, and somehow I find myself still wondering when to use one or the other. The folks at #kiteworks have a nice article: https://gag.gl/oi1wa2 SFTP (Secure File Transfer Protocol) offers a highly secure method for transferring files by using encryption, authentication, and data integrity checks, making it a significant improvement over the older, unencrypted FTP protocol. However, it comes with drawbacks such as increased complexity, slower performance, and limited native support for compliance features like audit logging or encryption at rest. Tools like Kiteworks enhance SFTP by adding security, governance, and compliance capabilities, making it more suitable for regulated environments.

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Regarding the AI disruption of SW freakout - I am interested in knowing if there is demand/supply based market analysis of this? If so, please do comment and let me know. The current repricing of SW seems to assume SW is demand constrained?? - i.e. the same amount of SW can be generated much cheaper, and thus software vendors will face pricing pressure and competition. But what if SW is supply constrained? Why not a scenario where 10 times as much software can be sold for 2 times as much revenue using the same number of software engineers? Considering the premium multiple that SW companies have commanded, and the high salaries of SW engineers, I would guess that the market is more likely to be supply constrained.

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