The Steady Beat - Issue 24.7.2
Lines-of-code metrics don't matter, injecting AI into product ops, getting engineers on the product train, and accounting for UX bias.
Welcome to The Steady Beat, a weekly-ish round-up of hand-picked articles and resources for people who make software products: designers, engineers, product managers, and organizational leaders.
By the numbers - the Bell System
470B - AT&T’s assets in 1974 were valued at $74 billion (about $470 billion in 2024 dollars), more than three times the assets of the next largest company, General Motors.
500M - In 1975, AT&T handled approximately 500 million telephone calls a day, up from over 50 million calls a day in 1925 and five million in 1900.
70B - Between 1960 and 1973, AT&T spent nearly $70 billion on new telephone infrastructure, compared to NASA’s $26 billion for the Apollo Program during the same period.
91% - By 1930, the Bell system connected to 91% of the telephones on earth, with New York State alone having as many phones as all of Europe by the end of the 19th century.
98% - Between 1925 and 1981, the cost of a 3-minute coast-to-coast phone call fell by approximately 98%, while the number of phones per 100 population in the U.S. rose from 14 to 84.
Construction Physics, 11m
Riding the AI wave: transforming product ops
In a deep dive into how AI can revolutionize product operations, this third installment of a series on the product operating model covers the essentials of navigating disruptive tech, learning from Jeffrey Moore’s insights, and enhancing product management frameworks pioneered by Melissa Perri. With AI, companies can streamline decision-making, improve customer interactions, and optimize processes. Real-world examples, like Intercom’s pivot with AI-driven Fin and Steady’s Continuous Coordination, highlight practical applications. The session also touched on the strategic use of AI to enhance productivity without overhauling business models, emphasizing the importance of staying ahead of the curve in this rapidly evolving landscape.
John Johnson, PMP CSM SPC, University of Maryland, 54m - #productmanagement #ai
Engineers: think product first
Ryan Peterman learned an invaluable lesson at Meta: even the most hardcore infrastructure engineers should embrace product thinking. By understanding the user impact of their work, engineers can prioritize better and deliver real value. Whether it’s optimizing database reads or enhancing user flows, this approach transforms good engineers into great ones. Channeling John Carmack’s wisdom, Ryan argues that focusing solely on code isn’t enough—being product-minded complements technical skills and can elevate one’s career. And dogfooding your product is a must.
The Developing Dev, 6m - #development #engineering
Cognitive bias in UX design
Even designers fall prey to cognitive biases, especially framing effects, which can skew their decisions. Framing is about context—how information is presented shapes our conclusions. A study showed that 51% of UX practitioners advocated for redesign when told 4 out of 20 users couldn’t find a feature, compared to just 39% when told 16 out of 20 could. This discrepancy highlights how positive vs. negative framing sways judgments. To counteract bias, resist snap judgments, gather more context, and experiment with different frames.
NN/g, 5m - #design #ux
Measure what matters
In the ever-confusing world of project management, Jim, a sales-turned-tech leader, felt like his team’s progress reports were two steps back for every step forward. His team tracked lines of code, story points, and earned value from finished points, but none of these activity-based metrics clarified actual progress. The truth? Real progress hinges on user-focused deliverables and the speed of their delivery. Technical stories and activity measures lead to late feedback and postponed learning. Johanna’s advice: focus on cycle time and deliverables that change user interaction, and measure progress through flow metrics and demos.
Johanna Rothman, 7m #leadership #management
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