OpenAI’s study indicates that users primarily utilize ChatGPT as an advisory tool to enhance decision-making and productivity in knowledge-intensive jobs.

In my searches/research, analysts like Gartner, IDC, and Forrester are invisible to ChatGTP, Claude, etc. The robots will find the reports licensed by vendors - I’m guessing only if they don’t require leadgen. The analysts should probably start publishing some juicy abstracts with just enough numbers, analysis, and advice to get into the training data and search results to get people to funnel to their pages. Of course, converting a rando-click to a $95,000 PDF would be some real funnel-management magic.

Goldman Sachs has implemented its GS AI Assistant to enhance employee efficiency in tasks like document summarization and data analysis, while acknowledging the importance of personal nuance in client services.

Somewhere between 40% and 60% of apps run on private cloud, you just never hear about it.

I’m thinking through the topic of ROI for infrastructure software again (obviously for our PaaS stuff). I get asked about this every year or two. Each time I look at it, I get more confused. This ask always come from sales, presumably because the buyers are asking it. But, I don’t think the ask is about the basic ROI calculations: the amount we paid for this tech is less than the amount of money we make using it.

An enterprise AI strategy probably means adding AI to your existing apps and workflows, not just standing up a stand-alone AI app. We experience generative AI as chatbots in the consumer space - and they’re great! - but this doesn’t seem like the best approach for business applications. Think about search. We don’t even notice it now, but at work, search is built into existing apps, it’s not usually a stand-alone app that tightly integrates with and links into existing apps.

Much like financial debt, technical debt is helpful when managed responsibly, but like real debt, tech debt can also stop growth and innovation in its tracks. My colleague Bryan Ross has a piece out on tech debt with five ways to address is. Here’s a summary of it. Think of technical debt as the accumulation of compromises on development quality to save time. Some examples of these choices are postponing unit tests, running outdated software, or focusing on end-user features at the expense of internal processes.

From the report “Top Strategic Technology Trends for 2023: Platform Engineering,” Paul Delory and Oleksandr Matvitskyy, Gartner, Oct 2022. The authors don’t take a strong position here (?), but I think their vision of platform engineering sits above the infrastructure layer. See the diagram above, for example. The platform engineering group doesn’t mess with that stuff. This seems right to me. Everyone loves a Gartner prediction: “By 2026, 80% of software engineering organizations will establish platform teams as internal providers of reusable services, components and tools for application delivery.

I’ve tracked at least three different definitions of DevOps since the days of “agile infrastructure”: Using Puppet and Chef (and then Ansible and Chef) to replace Opsware and BladeLogic. Full stack engineers to setup EC2, load-balancers, and other Morlock shit. Full stack engineers are bad, but sort of the same thing. Also, you can’t have a DevOps “group” or title. But, you know, someone should do all that automation. Putting all the people on one team, having them focus on a product, and establishing a culture of caring and learning.